diff --git "a/notebooks/finetuning_commafixer_with_LoRa.ipynb" "b/notebooks/finetuning_commafixer_with_LoRa.ipynb" --- "a/notebooks/finetuning_commafixer_with_LoRa.ipynb" +++ "b/notebooks/finetuning_commafixer_with_LoRa.ipynb" @@ -1,59 +1,55 @@ { - "cells":[ + "cells": [ { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "## Fine-tuning RoBERTa large for token classification\n", "\n", "Treats fixing commas as a NER problem, where for each token we predict whether a comma should be inserted after it. We assume input data has no commas, which ensures the input distribution is the same for the model, regardless of the types of mistakes users could make. The model would then restore the commas and leave the rest of the text intact." ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"dyUJpYnHWmCybknBwWLe5Z", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "dyUJpYnHWmCybknBwWLe5Z", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "import torch\n", "torch.cuda.is_available()" ], - "execution_count":1, - "outputs":[ + "execution_count": 1, + "outputs": [ { - "data":{ - "text\/plain":[ + "data": { + "text/plain": [ "True" ] }, - "metadata":{ - - }, - "output_type":"display_data" + "metadata": {}, + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"dwzLkm8DtZ6gwwOBT2Y7rM", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"9tLzdz03okTFWD3Vy0h4vS" + "metadata": { + "datalore": { + "node_id": "dwzLkm8DtZ6gwwOBT2Y7rM", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "9tLzdz03okTFWD3Vy0h4vS" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "from datasets import load_dataset\n", "from transformers import (\n", " AutoModelForTokenClassification,\n", @@ -69,209 +65,203 @@ "import re\n", "import evaluate" ], - "execution_count":2, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"kmU9kledu94gR9zgAN1Ga5", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"h8AdYv2Y3tw7Yb4W3IGoxc" + "execution_count": 2, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "kmU9kledu94gR9zgAN1Ga5", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "h8AdYv2Y3tw7Yb4W3IGoxc" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "model_checkpoint = \"roberta-large\"" ], - "execution_count":3, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"t1hpVurdN6Ji6IPIUFlqnB", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"sGTotT7rOtZWKTPgVm4gX6" + "execution_count": 3, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "t1hpVurdN6Ji6IPIUFlqnB", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "sGTotT7rOtZWKTPgVm4gX6" } } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "We will use the wikitext dataset, since it is large and has more diverse texts than, e.g., books, with fairly a lot of commas." ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"1L9Lgn5UOmYdIAB9VCLOUx", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "1L9Lgn5UOmYdIAB9VCLOUx", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "wikitext = load_dataset('wikitext', 'wikitext-103-v1') # TODO we should only load part of it, too big to train on whole anyway" ], - "execution_count":4, - "outputs":[ + "execution_count": 4, + "outputs": [ { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"b49ef90878574db2b0ee3e832b51f0e1" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "b49ef90878574db2b0ee3e832b51f0e1" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"EzocdU7ZnKbRTA6Qyb2eui" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "EzocdU7ZnKbRTA6Qyb2eui" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"b73ea2c1d22f48a588d04f63ae856ad6" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "b73ea2c1d22f48a588d04f63ae856ad6" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"5EyBntpe2omeh7zplkVxbz" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "5EyBntpe2omeh7zplkVxbz" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"4df35e53a0cc41118cf794e623c9e1ae" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "4df35e53a0cc41118cf794e623c9e1ae" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"YPtbQNg3pL68UM9OfASsNu" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "YPtbQNg3pL68UM9OfASsNu" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"d4f75e8ad8ae4b729303ccbe2283de3c" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "d4f75e8ad8ae4b729303ccbe2283de3c" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"mYXcmC0ka6gJT8jwrSYZGF" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "mYXcmC0ka6gJT8jwrSYZGF" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"10f1853410664a0b9cc9ea8693a64807" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "10f1853410664a0b9cc9ea8693a64807" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"tvLSuVyurIDaBZbUl4aC8p" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "tvLSuVyurIDaBZbUl4aC8p" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"f2d9c2140b3f4e068382f63a376c8cb1" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f2d9c2140b3f4e068382f63a376c8cb1" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"SjJATq4Bu7Rd02ryJBysxk" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "SjJATq4Bu7Rd02ryJBysxk" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"f69e84b3b9634448b0141e11236366c3" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f69e84b3b9634448b0141e11236366c3" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"kLgRm1cp86gQOV4nvn0nsn" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "kLgRm1cp86gQOV4nvn0nsn" } } }, - "output_type":"display_data" + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"cmS6SmnZ5bCm9dS4HcW3yj", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"yuWdT9AFjYlOMQTPOZZIUT" + "metadata": { + "datalore": { + "node_id": "cmS6SmnZ5bCm9dS4HcW3yj", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "yuWdT9AFjYlOMQTPOZZIUT" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "wikitext" ], - "execution_count":5, - "outputs":[ + "execution_count": 5, + "outputs": [ { - "data":{ - "text\/plain":[ + "data": { + "text/plain": [ "DatasetDict({\n", " test: Dataset({\n", " features: ['text'],\n", @@ -288,44 +278,40 @@ "})" ] }, - "metadata":{ - - }, - "output_type":"display_data" + "metadata": {}, + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"gL2NZUdQOETnRVwSrzZMK6", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"gE1CBjdhArw5iApIeceEYR" + "metadata": { + "datalore": { + "node_id": "gL2NZUdQOETnRVwSrzZMK6", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "gE1CBjdhArw5iApIeceEYR" } } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "### Preprocessing" ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"TEOArfY5ox2vtfEeWFofK8", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "TEOArfY5ox2vtfEeWFofK8", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "label_list = [\n", " \"O\",\n", " \"B-COMMA\",\n", @@ -339,42 +325,38 @@ " \"B-COMMA\": 1\n", "}" ], - "execution_count":6, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"mtshTbrO5e9UwX0mQbohcf", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"pW8zIwKks0yGy8XVO6sU9T" + "execution_count": 6, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "mtshTbrO5e9UwX0mQbohcf", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "pW8zIwKks0yGy8XVO6sU9T" } } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "Wikitext is already space tokenized. We use that information, remove commas from the data and append a COMMA tag to the preceding token." ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"cwIAYXqprU9uW3eS1d7ii6", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "cwIAYXqprU9uW3eS1d7ii6", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "def map_wikitext(x) -> dict:\n", " tokens = x[\"text\"].split()\n", " new_tokens, labels = [], []\n", @@ -389,212 +371,205 @@ " new_tokens.append(token)\n", " return {'tokens': new_tokens, 'tags': labels}" ], - "execution_count":7, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"HD0Jlp59AnlQFmXvPheL48", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"u4ni0ncMGCkPMDSQTb2Fjz" + "execution_count": 7, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "HD0Jlp59AnlQFmXvPheL48", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "u4ni0ncMGCkPMDSQTb2Fjz" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "wikitext[\"train\"][3]" ], - "execution_count":8, - "outputs":[ + "execution_count": 8, + "outputs": [ { - "data":{ - "text\/plain":[ + "data": { + "text/plain": [ "{'text': ' Senjō no Valkyria 3 : Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to as Valkyria Chronicles III outside Japan , is a tactical role @-@ playing video game developed by Sega and Media.Vision for the PlayStation Portable . Released in January 2011 in Japan , it is the third game in the Valkyria series . Employing the same fusion of tactical and real @-@ time gameplay as its predecessors , the story runs parallel to the first game and follows the \" Nameless \" , a penal military unit serving the nation of Gallia during the Second Europan War who perform secret black operations and are pitted against the Imperial unit \" Raven \" . \\n'}" ] }, - "metadata":{ - - }, - "output_type":"display_data" + "metadata": {}, + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"vURwu7beQTkZL5mzHlfAjT", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"i6bHaRjA1inmjze1Y7w5KP" + "metadata": { + "datalore": { + "node_id": "vURwu7beQTkZL5mzHlfAjT", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "i6bHaRjA1inmjze1Y7w5KP" } } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "Other than mapping, we also filter empty texts (25% in wikitext), and very long paragraphs. We print texts starting with a comma, and remove the initial comma since we cannot represent it and assume no sentene should start with a comma." ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"YjN1hYHVnzLmr0byD0haSW", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "YjN1hYHVnzLmr0byD0haSW", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "wikitext_mapped = wikitext.filter(lambda x: x[\"text\"] and len(x[\"text\"].split()) < 512).map(map_wikitext)" ], - "execution_count":9, - "outputs":[ + "execution_count": 9, + "outputs": [ { - "name":"stdout", - "text":[ + "name": "stdout", + "text": [ " , \n", "\n", - " , the slight increase in comparison loop efficiency does not compensate for the extra iteration . Knuth 1998 gives a value of \n", - "\n" + " , the slight increase in comparison loop efficiency does not compensate for the extra iteration . Knuth 1998 gives a value of \n" ], - "output_type":"stream" + "output_type": "stream" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"4570a228ecd043198331a1378d9f9621" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "4570a228ecd043198331a1378d9f9621" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"zkpNXnOddd31KKr2PbuQbu" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "zkpNXnOddd31KKr2PbuQbu" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"73a77fce14294f9d9d1de72cbdb93bac" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "73a77fce14294f9d9d1de72cbdb93bac" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"oMvc26V11XATUcJL3MpaSx" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "oMvc26V11XATUcJL3MpaSx" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"85c2b88a530b4b10b836185d0fbc4f38" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "85c2b88a530b4b10b836185d0fbc4f38" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"3oLW1dUwUPqA7rzPEXv5I8" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "3oLW1dUwUPqA7rzPEXv5I8" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"9024a3a6a42c4bc3b0afdc30557ad09d" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "9024a3a6a42c4bc3b0afdc30557ad09d" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"MRLrkIa0FFrxwmmKmV2Hc2" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "MRLrkIa0FFrxwmmKmV2Hc2" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"1e08db6867a44093b73a3d78f92612d6" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "1e08db6867a44093b73a3d78f92612d6" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"3eCfbu6IspjzG6HvvX24Td" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "3eCfbu6IspjzG6HvvX24Td" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"e26308dff4c3457794b8cca3bb455485" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "e26308dff4c3457794b8cca3bb455485" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"JxGyWtrVY3EqxihT5S21Z6" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "JxGyWtrVY3EqxihT5S21Z6" } } }, - "output_type":"display_data" + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"prJX5QZg1VmAX6pN0mnyom", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"48CMjhbFyYMCFfudw3NWLS" + "metadata": { + "datalore": { + "node_id": "prJX5QZg1VmAX6pN0mnyom", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "48CMjhbFyYMCFfudw3NWLS" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "wikitext_mapped[\"train\"][1]" ], - "execution_count":10, - "outputs":[ + "execution_count": 10, + "outputs": [ { - "data":{ - "text\/plain":[ + "data": { + "text/plain": [ "{'text': ' Senjō no Valkyria 3 : Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to as Valkyria Chronicles III outside Japan , is a tactical role @-@ playing video game developed by Sega and Media.Vision for the PlayStation Portable . Released in January 2011 in Japan , it is the third game in the Valkyria series . Employing the same fusion of tactical and real @-@ time gameplay as its predecessors , the story runs parallel to the first game and follows the \" Nameless \" , a penal military unit serving the nation of Gallia during the Second Europan War who perform secret black operations and are pitted against the Imperial unit \" Raven \" . \\n',\n", " 'tokens': ['Senjō',\n", " 'no',\n", @@ -840,81 +815,76 @@ " 0]}" ] }, - "metadata":{ - - }, - "output_type":"display_data" + "metadata": {}, + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"DCZXZMrMQzYcH8CUXHrd1E", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"jCn3aXRNlHZ54hwSvCOPY1" + "metadata": { + "datalore": { + "node_id": "DCZXZMrMQzYcH8CUXHrd1E", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "jCn3aXRNlHZ54hwSvCOPY1" } } } }, { - "cell_type":"markdown", - "source":[ - - ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"ucTrLhx3rypOaFAVs5dAeO", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "cell_type": "markdown", + "source": [], + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "ucTrLhx3rypOaFAVs5dAeO", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "seqeval = evaluate.load(\"seqeval\")" ], - "execution_count":11, - "outputs":[ + "execution_count": 11, + "outputs": [ { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"789a5c3db5704cd288d1e9f55d758813" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "789a5c3db5704cd288d1e9f55d758813" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"K630mpF3l5do8ry0ZFvxvF" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "K630mpF3l5do8ry0ZFvxvF" } } }, - "output_type":"display_data" + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"tW4ZovAZV62tVCQ5pZtFd0", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"u9kmEPsjJjMXH8QxYxCkzI" + "metadata": { + "datalore": { + "node_id": "tW4ZovAZV62tVCQ5pZtFd0", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "u9kmEPsjJjMXH8QxYxCkzI" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ + "# TODO only compute for B-COMMA, not overall\n", "def compute_metrics(p):\n", " predictions, labels = p\n", " predictions = np.argmax(predictions, axis=2)\n", @@ -936,142 +906,136 @@ " \"accuracy\": results[\"overall_accuracy\"],\n", " }" ], - "execution_count":12, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"rRWP0oFCa1osvkitTVt0mM", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"w9WiJH27gOJfWxpv8b8zZU" + "execution_count": 12, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "rRWP0oFCa1osvkitTVt0mM", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "w9WiJH27gOJfWxpv8b8zZU" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "tokenizer = AutoTokenizer.from_pretrained('roberta-large', add_prefix_space=True)\n", "tokenizer" ], - "execution_count":13, - "outputs":[ + "execution_count": 13, + "outputs": [ { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"980c254886c1489197bd9ca70d207508" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "980c254886c1489197bd9ca70d207508" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"nF2Su6O3raZwjaNRPC85xu" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "nF2Su6O3raZwjaNRPC85xu" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"d260f0b9f950478c907b62576ddac227" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "d260f0b9f950478c907b62576ddac227" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"dEDwPE4KTC0tVSGuAcl72X" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "dEDwPE4KTC0tVSGuAcl72X" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"a656fa8f2d91428eb812643b8227dc16" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "a656fa8f2d91428eb812643b8227dc16" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"w7Ii5jWzd4jT9idw2faCzz" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "w7Ii5jWzd4jT9idw2faCzz" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"396a09f765c74d01a69abd7c2b4c6dfc" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "396a09f765c74d01a69abd7c2b4c6dfc" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"n3ZrIydi6O5i4meoL30arw" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "n3ZrIydi6O5i4meoL30arw" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "text\/plain":[ - "RobertaTokenizerFast(name_or_path='roberta-large', vocab_size=50265, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '<\/s>', 'unk_token': '', 'sep_token': '<\/s>', 'pad_token': '', 'cls_token': '', 'mask_token': AddedToken(\"\", rstrip=False, lstrip=True, single_word=False, normalized=False)}, clean_up_tokenization_spaces=True)" + "data": { + "text/plain": [ + "RobertaTokenizerFast(name_or_path='roberta-large', vocab_size=50265, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '', 'sep_token': '', 'pad_token': '', 'cls_token': '', 'mask_token': AddedToken(\"\", rstrip=False, lstrip=True, single_word=False, normalized=False)}, clean_up_tokenization_spaces=True)" ] }, - "metadata":{ - - }, - "output_type":"display_data" + "metadata": {}, + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"NJFxvxn5BnPxJcgxtURDRo", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"KFRHM0usfnGQnFdbZihU7B" + "metadata": { + "datalore": { + "node_id": "NJFxvxn5BnPxJcgxtURDRo", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "KFRHM0usfnGQnFdbZihU7B" } } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "We need to map the space-tokenized wikitext to the roberta tokenization, together with the token tags. -100 is ignored by PyTorch during gradient computation, and is commonly used for special tokens ( and such) and additional tokens that appear in the middle of words due to wordpiece tokenization." ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"I0EKhbKB1hQPTOpfNyatvD", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "I0EKhbKB1hQPTOpfNyatvD", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "def tokenize_and_align_labels(examples):\n", " tokenized_inputs = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True)\n", "\n", @@ -1093,125 +1057,121 @@ " tokenized_inputs[\"labels\"] = labels\n", " return tokenized_inputs" ], - "execution_count":14, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"39laMNR7YdJWePIs3R1jgF", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"Uegyfkvr1Grti6ZGTnIIEw" + "execution_count": 14, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "39laMNR7YdJWePIs3R1jgF", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "Uegyfkvr1Grti6ZGTnIIEw" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "tokenized_wikitext = wikitext_mapped.map(tokenize_and_align_labels, batched=True)" ], - "execution_count":15, - "outputs":[ + "execution_count": 15, + "outputs": [ { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"d1c9b314cd8f4bddbc673a01c7f8d371" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "d1c9b314cd8f4bddbc673a01c7f8d371" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"40xVPDkPm35WbYnvA1y8Ts" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "40xVPDkPm35WbYnvA1y8Ts" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"ec21ec40a59449edb8f7d9b8c36c9263" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "ec21ec40a59449edb8f7d9b8c36c9263" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"bTYqFsVN6Du01ntyrDTYIO" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "bTYqFsVN6Du01ntyrDTYIO" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"40fde6fe7da148ecaed3eb5df360c348" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "40fde6fe7da148ecaed3eb5df360c348" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"JZhIN2xMwaecKDza6S8uZV" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "JZhIN2xMwaecKDza6S8uZV" } } }, - "output_type":"display_data" + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"Cxvaj6ae2iDlWrNtdzFvtR", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"trpuWhYCfTALVnH0ffydT9" + "metadata": { + "datalore": { + "node_id": "Cxvaj6ae2iDlWrNtdzFvtR", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "trpuWhYCfTALVnH0ffydT9" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "tokenized_wikitext = tokenized_wikitext.remove_columns('text')" ], - "execution_count":16, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"ZRFAWku3LxAczcB8WGMM8A", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"7NEFb43JKeUw4DZjeR1Qe8" + "execution_count": 16, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "ZRFAWku3LxAczcB8WGMM8A", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "7NEFb43JKeUw4DZjeR1Qe8" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "for input_id, label in zip(tokenized_wikitext[\"train\"][1]['input_ids'], tokenized_wikitext[\"train\"][1]['labels']):\n", " print(tokenizer.convert_ids_to_tokens(input_id), id2label[label])" ], - "execution_count":17, - "outputs":[ + "execution_count": 17, + "outputs": [ { - "name":"stdout", - "text":[ + "name": "stdout", + "text": [ " -100\n", "ĠSen 0\n", "j -100\n", @@ -1367,202 +1327,192 @@ "ĠRaven 0\n", "Ġ\" 0\n", "Ġ. 0\n", - "<\/s> -100\n" + " -100\n" ], - "output_type":"stream" + "output_type": "stream" } ], - "metadata":{ - "datalore":{ - "node_id":"yFJf4KbWqy5NZBDOa5CUJ7", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"PeaXXWTbo8nx301q3J7LYT" + "metadata": { + "datalore": { + "node_id": "yFJf4KbWqy5NZBDOa5CUJ7", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "PeaXXWTbo8nx301q3J7LYT" } } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "The collator automatically handles padding the tokens and labels inside batches" ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"VWf2JoJ24cwXU3AclqyUcM", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "VWf2JoJ24cwXU3AclqyUcM", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)" ], - "execution_count":18, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"zswKKFPLgf53urFtHqKcTk", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"Tx4nI6q73aLba0qtxPM3HO" + "execution_count": 18, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "zswKKFPLgf53urFtHqKcTk", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "Tx4nI6q73aLba0qtxPM3HO" } } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "### Training" ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"JOUeiDhDqOWHLSWnNxMUb8", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "JOUeiDhDqOWHLSWnNxMUb8", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "\n", "model = AutoModelForTokenClassification.from_pretrained(\n", " model_checkpoint, num_labels=len(label_list), id2label=id2label, label2id=label2id\n", ")" ], - "execution_count":19, - "outputs":[ + "execution_count": 19, + "outputs": [ { - "name":"stderr", - "text":[ + "name": "stderr", + "text": [ "Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ], - "output_type":"stream" + "output_type": "stream" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"02c337abd8a14ec1a9b812b83c5996f7" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "02c337abd8a14ec1a9b812b83c5996f7" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"BKS3VlmQIh5accoRARgGYI" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "BKS3VlmQIh5accoRARgGYI" } } }, - "output_type":"display_data" + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"rbqrUuMV3GkVdI2xPui57k", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"ej3yZyRCtCDWey9uBUDZ8i" + "metadata": { + "datalore": { + "node_id": "rbqrUuMV3GkVdI2xPui57k", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "ej3yZyRCtCDWey9uBUDZ8i" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "peft_config = LoraConfig(\n", " task_type=TaskType.TOKEN_CLS, inference_mode=False, r=16, lora_alpha=16, lora_dropout=0.1, bias=\"all\"\n", ")" ], - "execution_count":20, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"2OJ85GJzkPEfzNF1FZOsWr", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"rPZGw8diH0GN6aP7FKP8ca" + "execution_count": 20, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "2OJ85GJzkPEfzNF1FZOsWr", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "rPZGw8diH0GN6aP7FKP8ca" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "model = get_peft_model(model, peft_config)\n", "model.print_trainable_parameters()" ], - "execution_count":21, - "outputs":[ + "execution_count": 21, + "outputs": [ { - "name":"stdout", - "text":[ + "name": "stdout", + "text": [ "trainable params: 1,848,324 || all params: 355,887,108 || trainable%: 0.519356829301049\n" ], - "output_type":"stream" + "output_type": "stream" } ], - "metadata":{ - "datalore":{ - "node_id":"unCtyhS3AGHsrOijLzaXZI", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"9b8k4ntwhjIkmCPW6b5sUf" + "metadata": { + "datalore": { + "node_id": "unCtyhS3AGHsrOijLzaXZI", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "9b8k4ntwhjIkmCPW6b5sUf" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "lr = 1e-3\n", "batch_size = 8" ], - "execution_count":22, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"xY131yxZKOOEzt1z0XWzQS", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"Q4UIhvaDzVEpSjVk1zzHDy" + "execution_count": 22, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "xY131yxZKOOEzt1z0XWzQS", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "Q4UIhvaDzVEpSjVk1zzHDy" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "training_args = TrainingArguments(\n", " output_dir=\"roberta-large-lora-token-classification\",\n", " learning_rate=lr,\n", @@ -1581,25 +1531,23 @@ " load_best_model_at_end=True,\n", ")" ], - "execution_count":23, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"ZPXLuGOcwlGRrrSv3yuRcc", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"sgKkzWyaxUFUAVeYMkmETq" + "execution_count": 23, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "ZPXLuGOcwlGRrrSv3yuRcc", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "sgKkzWyaxUFUAVeYMkmETq" } } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", @@ -1612,22 +1560,22 @@ "\n", "trainer.train()" ], - "execution_count":24, - "outputs":[ + "execution_count": 24, + "outputs": [ { - "name":"stderr", - "text":[ - "\/opt\/python\/envs\/default\/lib\/python3.8\/site-packages\/transformers\/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", + "name": "stderr", + "text": [ + "/opt/python/envs/default/lib/python3.8/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n", "You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n" ], - "output_type":"stream" + "output_type": "stream" }, { - "ename":"KeyboardInterrupt", - "evalue":"KeyboardInterrupt: ", - "traceback":[ - "\u001b[0;31m---------------------------------------------------------------------------", + "ename": "KeyboardInterrupt", + "evalue": "KeyboardInterrupt: ", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------", "Traceback (most recent call last)", " at line 11 in ", " at line 1539 in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)", @@ -1638,511 +1586,507 @@ " at line 200 in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)", "KeyboardInterrupt: " ], - "output_type":"error" + "output_type": "error" }, { - "data":{ - "text\/html":[ + "data": { + "text/html": [ "\n", "
\n", " \n", - " <\/progress>\n", - " [ 4605\/20000 5:12:54 < 17:26:33, 0.25 it\/s, Epoch 0.13\/1]\n", - " <\/div>\n", + " \n", + " [ 4605/20000 5:12:54 < 17:26:33, 0.25 it/s, Epoch 0.13/1]\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Step<\/th>\n", - " Training Loss<\/th>\n", - " Validation Loss<\/th>\n", - " Precision<\/th>\n", - " Recall<\/th>\n", - " F1<\/th>\n", - " Accuracy<\/th>\n", - " <\/tr>\n", - " <\/thead>\n", + " StepTraining LossValidation LossPrecisionRecallF1Accuracy
100<\/td>\n", - " 0.082400<\/td>\n", - " 0.071184<\/td>\n", - " 0.738182<\/td>\n", - " 0.753200<\/td>\n", - " 0.745615<\/td>\n", - " 0.973547<\/td>\n", - " <\/tr>\n", + " 1000.0824000.0711840.7381820.7532000.7456150.973547
200<\/td>\n", - " 0.062200<\/td>\n", - " 0.051519<\/td>\n", - " 0.803700<\/td>\n", - " 0.850525<\/td>\n", - " 0.826450<\/td>\n", - " 0.981614<\/td>\n", - " <\/tr>\n", + " 2000.0622000.0515190.8037000.8505250.8264500.981614
300<\/td>\n", - " 0.049100<\/td>\n", - " 0.044637<\/td>\n", - " 0.821739<\/td>\n", - " 0.858548<\/td>\n", - " 0.839740<\/td>\n", - " 0.983133<\/td>\n", - " <\/tr>\n", + " 3000.0491000.0446370.8217390.8585480.8397400.983133
400<\/td>\n", - " 0.046000<\/td>\n", - " 0.043286<\/td>\n", - " 0.827163<\/td>\n", - " 0.855683<\/td>\n", - " 0.841181<\/td>\n", - " 0.983369<\/td>\n", - " <\/tr>\n", + " 4000.0460000.0432860.8271630.8556830.8411810.983369
500<\/td>\n", - " 0.049700<\/td>\n", - " 0.043400<\/td>\n", - " 0.815975<\/td>\n", - " 0.873257<\/td>\n", - " 0.843645<\/td>\n", - " 0.983340<\/td>\n", - " <\/tr>\n", + " 5000.0497000.0434000.8159750.8732570.8436450.983340
600<\/td>\n", - " 0.047900<\/td>\n", - " 0.043947<\/td>\n", - " 0.790265<\/td>\n", - " 0.908691<\/td>\n", - " 0.845351<\/td>\n", - " 0.982887<\/td>\n", - " <\/tr>\n", + " 6000.0479000.0439470.7902650.9086910.8453510.982887
700<\/td>\n", - " 0.042500<\/td>\n", - " 0.040706<\/td>\n", - " 0.846508<\/td>\n", - " 0.843840<\/td>\n", - " 0.845171<\/td>\n", - " 0.984087<\/td>\n", - " <\/tr>\n", + " 7000.0425000.0407060.8465080.8438400.8451710.984087
800<\/td>\n", - " 0.045500<\/td>\n", - " 0.040963<\/td>\n", - " 0.845999<\/td>\n", - " 0.845272<\/td>\n", - " 0.845636<\/td>\n", - " 0.984116<\/td>\n", - " <\/tr>\n", + " 8000.0455000.0409630.8459990.8452720.8456360.984116
900<\/td>\n", - " 0.050000<\/td>\n", - " 0.042453<\/td>\n", - " 0.852265<\/td>\n", - " 0.831996<\/td>\n", - " 0.842009<\/td>\n", - " 0.983929<\/td>\n", - " <\/tr>\n", + " 9000.0500000.0424530.8522650.8319960.8420090.983929
1000<\/td>\n", - " 0.050800<\/td>\n", - " 0.042836<\/td>\n", - " 0.860358<\/td>\n", - " 0.803247<\/td>\n", - " 0.830822<\/td>\n", - " 0.983163<\/td>\n", - " <\/tr>\n", + " 10000.0508000.0428360.8603580.8032470.8308220.983163
1100<\/td>\n", - " 0.049100<\/td>\n", - " 0.043222<\/td>\n", - " 0.805093<\/td>\n", - " 0.896753<\/td>\n", - " 0.848455<\/td>\n", - " 0.983512<\/td>\n", - " <\/tr>\n", + " 11000.0491000.0432220.8050930.8967530.8484550.983512
1200<\/td>\n", - " 0.043400<\/td>\n", - " 0.043033<\/td>\n", - " 0.872473<\/td>\n", - " 0.803725<\/td>\n", - " 0.836689<\/td>\n", - " 0.983851<\/td>\n", - " <\/tr>\n", + " 12000.0434000.0430330.8724730.8037250.8366890.983851
1300<\/td>\n", - " 0.043900<\/td>\n", - " 0.039672<\/td>\n", - " 0.849282<\/td>\n", - " 0.847660<\/td>\n", - " 0.848470<\/td>\n", - " 0.984416<\/td>\n", - " <\/tr>\n", + " 13000.0439000.0396720.8492820.8476600.8484700.984416
1400<\/td>\n", - " 0.049900<\/td>\n", - " 0.041815<\/td>\n", - " 0.885053<\/td>\n", - " 0.788348<\/td>\n", - " 0.833906<\/td>\n", - " 0.983836<\/td>\n", - " <\/tr>\n", + " 14000.0499000.0418150.8850530.7883480.8339060.983836
1500<\/td>\n", - " 0.044700<\/td>\n", - " 0.041055<\/td>\n", - " 0.849876<\/td>\n", - " 0.850525<\/td>\n", - " 0.850200<\/td>\n", - " 0.984573<\/td>\n", - " <\/tr>\n", + " 15000.0447000.0410550.8498760.8505250.8502000.984573
1600<\/td>\n", - " 0.045100<\/td>\n", - " 0.040592<\/td>\n", - " 0.847970<\/td>\n", - " 0.853964<\/td>\n", - " 0.850957<\/td>\n", - " 0.984603<\/td>\n", - " <\/tr>\n", + " 16000.0451000.0405920.8479700.8539640.8509570.984603
1700<\/td>\n", - " 0.042900<\/td>\n", - " 0.040426<\/td>\n", - " 0.837404<\/td>\n", - " 0.871156<\/td>\n", - " 0.853946<\/td>\n", - " 0.984662<\/td>\n", - " <\/tr>\n", + " 17000.0429000.0404260.8374040.8711560.8539460.984662
1800<\/td>\n", - " 0.044800<\/td>\n", - " 0.041155<\/td>\n", - " 0.807739<\/td>\n", - " 0.897230<\/td>\n", - " 0.850136<\/td>\n", - " 0.983718<\/td>\n", - " <\/tr>\n", + " 18000.0448000.0411550.8077390.8972300.8501360.983718
1900<\/td>\n", - " 0.050600<\/td>\n", - " 0.039829<\/td>\n", - " 0.866008<\/td>\n", - " 0.829035<\/td>\n", - " 0.847119<\/td>\n", - " 0.984598<\/td>\n", - " <\/tr>\n", + " 19000.0506000.0398290.8660080.8290350.8471190.984598
2000<\/td>\n", - " 0.046400<\/td>\n", - " 0.039029<\/td>\n", - " 0.847822<\/td>\n", - " 0.855110<\/td>\n", - " 0.851450<\/td>\n", - " 0.984642<\/td>\n", - " <\/tr>\n", + " 20000.0464000.0390290.8478220.8551100.8514500.984642
2100<\/td>\n", - " 0.044200<\/td>\n", - " 0.038947<\/td>\n", - " 0.846089<\/td>\n", - " 0.858453<\/td>\n", - " 0.852226<\/td>\n", - " 0.984677<\/td>\n", - " <\/tr>\n", + " 21000.0442000.0389470.8460890.8584530.8522260.984677
2200<\/td>\n", - " 0.039900<\/td>\n", - " 0.039619<\/td>\n", - " 0.824704<\/td>\n", - " 0.879370<\/td>\n", - " 0.851160<\/td>\n", - " 0.984170<\/td>\n", - " <\/tr>\n", + " 22000.0399000.0396190.8247040.8793700.8511600.984170
2300<\/td>\n", - " 0.045400<\/td>\n", - " 0.040354<\/td>\n", - " 0.817536<\/td>\n", - " 0.889685<\/td>\n", - " 0.852086<\/td>\n", - " 0.984102<\/td>\n", - " <\/tr>\n", + " 23000.0454000.0403540.8175360.8896850.8520860.984102
2400<\/td>\n", - " 0.045100<\/td>\n", - " 0.040709<\/td>\n", - " 0.810662<\/td>\n", - " 0.884527<\/td>\n", - " 0.845985<\/td>\n", - " 0.983423<\/td>\n", - " <\/tr>\n", + " 24000.0451000.0407090.8106620.8845270.8459850.983423
2500<\/td>\n", - " 0.043700<\/td>\n", - " 0.040959<\/td>\n", - " 0.829169<\/td>\n", - " 0.878032<\/td>\n", - " 0.852902<\/td>\n", - " 0.984411<\/td>\n", - " <\/tr>\n", + " 25000.0437000.0409590.8291690.8780320.8529020.984411
2600<\/td>\n", - " 0.041000<\/td>\n", - " 0.039487<\/td>\n", - " 0.823321<\/td>\n", - " 0.887488<\/td>\n", - " 0.854201<\/td>\n", - " 0.984406<\/td>\n", - " <\/tr>\n", + " 26000.0410000.0394870.8233210.8874880.8542010.984406
2700<\/td>\n", - " 0.046600<\/td>\n", - " 0.041066<\/td>\n", - " 0.819955<\/td>\n", - " 0.875167<\/td>\n", - " 0.846662<\/td>\n", - " 0.983684<\/td>\n", - " <\/tr>\n", + " 27000.0466000.0410660.8199550.8751670.8466620.983684
2800<\/td>\n", - " 0.047400<\/td>\n", - " 0.039801<\/td>\n", - " 0.836634<\/td>\n", - " 0.871633<\/td>\n", - " 0.853775<\/td>\n", - " 0.984632<\/td>\n", - " <\/tr>\n", + " 28000.0474000.0398010.8366340.8716330.8537750.984632
2900<\/td>\n", - " 0.045500<\/td>\n", - " 0.038757<\/td>\n", - " 0.845130<\/td>\n", - " 0.855301<\/td>\n", - " 0.850185<\/td>\n", - " 0.984485<\/td>\n", - " <\/tr>\n", + " 29000.0455000.0387570.8451300.8553010.8501850.984485
3000<\/td>\n", - " 0.044300<\/td>\n", - " 0.039374<\/td>\n", - " 0.823618<\/td>\n", - " 0.885291<\/td>\n", - " 0.853342<\/td>\n", - " 0.984338<\/td>\n", - " <\/tr>\n", + " 30000.0443000.0393740.8236180.8852910.8533420.984338
3100<\/td>\n", - " 0.042900<\/td>\n", - " 0.041226<\/td>\n", - " 0.812402<\/td>\n", - " 0.890926<\/td>\n", - " 0.849854<\/td>\n", - " 0.983797<\/td>\n", - " <\/tr>\n", + " 31000.0429000.0412260.8124020.8909260.8498540.983797
3200<\/td>\n", - " 0.043900<\/td>\n", - " 0.038550<\/td>\n", - " 0.852317<\/td>\n", - " 0.848329<\/td>\n", - " 0.850318<\/td>\n", - " 0.984628<\/td>\n", - " <\/tr>\n", + " 32000.0439000.0385500.8523170.8483290.8503180.984628
3300<\/td>\n", - " 0.042000<\/td>\n", - " 0.040701<\/td>\n", - " 0.822446<\/td>\n", - " 0.882617<\/td>\n", - " 0.851470<\/td>\n", - " 0.984151<\/td>\n", - " <\/tr>\n", + " 33000.0420000.0407010.8224460.8826170.8514700.984151
3400<\/td>\n", - " 0.042000<\/td>\n", - " 0.040563<\/td>\n", - " 0.825961<\/td>\n", - " 0.873926<\/td>\n", - " 0.849267<\/td>\n", - " 0.984033<\/td>\n", - " <\/tr>\n", + " 34000.0420000.0405630.8259610.8739260.8492670.984033
3500<\/td>\n", - " 0.048000<\/td>\n", - " 0.039690<\/td>\n", - " 0.857677<\/td>\n", - " 0.832283<\/td>\n", - " 0.844789<\/td>\n", - " 0.984259<\/td>\n", - " <\/tr>\n", + " 35000.0480000.0396900.8576770.8322830.8447890.984259
3600<\/td>\n", - " 0.044000<\/td>\n", - " 0.039875<\/td>\n", - " 0.819356<\/td>\n", - " 0.884623<\/td>\n", - " 0.850739<\/td>\n", - " 0.984023<\/td>\n", - " <\/tr>\n", + " 36000.0440000.0398750.8193560.8846230.8507390.984023
3700<\/td>\n", - " 0.043300<\/td>\n", - " 0.039658<\/td>\n", - " 0.872338<\/td>\n", - " 0.817765<\/td>\n", - " 0.844171<\/td>\n", - " 0.984460<\/td>\n", - " <\/tr>\n", + " 37000.0433000.0396580.8723380.8177650.8441710.984460
3800<\/td>\n", - " 0.046400<\/td>\n", - " 0.039144<\/td>\n", - " 0.815661<\/td>\n", - " 0.890449<\/td>\n", - " 0.851416<\/td>\n", - " 0.984003<\/td>\n", - " <\/tr>\n", + " 38000.0464000.0391440.8156610.8904490.8514160.984003
3900<\/td>\n", - " 0.038300<\/td>\n", - " 0.039964<\/td>\n", - " 0.845676<\/td>\n", - " 0.858357<\/td>\n", - " 0.851969<\/td>\n", - " 0.984647<\/td>\n", - " <\/tr>\n", + " 39000.0383000.0399640.8456760.8583570.8519690.984647
4000<\/td>\n", - " 0.050700<\/td>\n", - " 0.038739<\/td>\n", - " 0.836861<\/td>\n", - " 0.864756<\/td>\n", - " 0.850580<\/td>\n", - " 0.984362<\/td>\n", - " <\/tr>\n", + " 40000.0507000.0387390.8368610.8647560.8505800.984362
4100<\/td>\n", - " 0.042500<\/td>\n", - " 0.038863<\/td>\n", - " 0.843916<\/td>\n", - " 0.867049<\/td>\n", - " 0.855326<\/td>\n", - " 0.984903<\/td>\n", - " <\/tr>\n", + " 41000.0425000.0388630.8439160.8670490.8553260.984903
4200<\/td>\n", - " 0.040000<\/td>\n", - " 0.039741<\/td>\n", - " 0.810694<\/td>\n", - " 0.897803<\/td>\n", - " 0.852028<\/td>\n", - " 0.983949<\/td>\n", - " <\/tr>\n", + " 42000.0400000.0397410.8106940.8978030.8520280.983949
4300<\/td>\n", - " 0.040800<\/td>\n", - " 0.038387<\/td>\n", - " 0.878170<\/td>\n", - " 0.806877<\/td>\n", - " 0.841015<\/td>\n", - " 0.984298<\/td>\n", - " <\/tr>\n", + " 43000.0408000.0383870.8781700.8068770.8410150.984298
4400<\/td>\n", - " 0.044800<\/td>\n", - " 0.038518<\/td>\n", - " 0.862983<\/td>\n", - " 0.836772<\/td>\n", - " 0.849675<\/td>\n", - " 0.984760<\/td>\n", - " <\/tr>\n", + " 44000.0448000.0385180.8629830.8367720.8496750.984760
4500<\/td>\n", - " 0.042700<\/td>\n", - " 0.039114<\/td>\n", - " 0.875372<\/td>\n", - " 0.815759<\/td>\n", - " 0.844515<\/td>\n", - " 0.984539<\/td>\n", - " <\/tr>\n", + " 45000.0427000.0391140.8753720.8157590.8445150.984539
4600<\/td>\n", - " 0.040000<\/td>\n", - " 0.039947<\/td>\n", - " 0.857752<\/td>\n", - " 0.845463<\/td>\n", - " 0.851563<\/td>\n", - " 0.984829<\/td>\n", - " <\/tr>\n", + " 46000.0400000.0399470.8577520.8454630.8515630.984829
4604<\/td>\n", - " 0.040000<\/td>\n", - " 0.039423<\/td>\n", - " 0.858057<\/td>\n", - " 0.845272<\/td>\n", - " 0.851617<\/td>\n", - " 0.984839<\/td>\n", - " <\/tr>\n", + " 46040.0400000.0394230.8580570.8452720.8516170.984839
4604<\/td>\n", - " 0.040000<\/td>\n", - " 0.037630<\/td>\n", - " 0.847159<\/td>\n", - " 0.851430<\/td>\n", - " 0.849289<\/td>\n", - " 0.984868<\/td>\n", - " <\/tr>\n", - " <\/tbody>\n", - "<\/table>

" + "

46040.0400000.0376300.8471590.8514300.8492890.984868

" ] }, - "metadata":{ - - }, - "output_type":"display_data" + "metadata": {}, + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"ONnqviSgLxFGZ6VsodfzN7", - "type":"CODE", - "hide_input_from_viewers":false, - "hide_output_from_viewers":false, - "report_properties":{ - "rowId":"pMxR5mn4hMefEjvfv5e39e" + "metadata": { + "datalore": { + "node_id": "ONnqviSgLxFGZ6VsodfzN7", + "type": "CODE", + "hide_input_from_viewers": false, + "hide_output_from_viewers": false, + "report_properties": { + "rowId": "pMxR5mn4hMefEjvfv5e39e" } } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "### Saving and evaluating the model" ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"H8JNhgIpWGBMp6DTrCC4WC", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "H8JNhgIpWGBMp6DTrCC4WC", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "trainer.evaluate(tokenized_wikitext[\"test\"])" ], - "execution_count":26, - "outputs":[ + "execution_count": 26, + "outputs": [ { - "data":{ - "text\/plain":[ + "data": { + "text/plain": [ "{'eval_loss': 0.037630438804626465,\n", " 'eval_precision': 0.8471585502984171,\n", " 'eval_recall': 0.8514300617230288,\n", @@ -2150,140 +2094,132 @@ " 'eval_accuracy': 0.9848677451373048}" ] }, - "metadata":{ - - }, - "output_type":"display_data" + "metadata": {}, + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"EdtPuNsMTuWUVQ3AqwHeDh", - "type":"CODE", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "metadata": { + "datalore": { + "node_id": "EdtPuNsMTuWUVQ3AqwHeDh", + "type": "CODE", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], - "execution_count":27, - "outputs":[ + "execution_count": 27, + "outputs": [ { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"aee257c0976d425da20f443a3973cf36" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "aee257c0976d425da20f443a3973cf36" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"WzG1d54t7f6rHW8IyvyquW" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "WzG1d54t7f6rHW8IyvyquW" } } }, - "output_type":"display_data" + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"B6WEXUADh8XfyBBfHBk309", - "type":"CODE", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "metadata": { + "datalore": { + "node_id": "B6WEXUADh8XfyBBfHBk309", + "type": "CODE", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ - "hub_name = \"klasocki\/roberta-large-lora-ner-comma-fixer\"" - ], - "execution_count":28, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"Dyz7jkJX7CngtBG5SfJB47", - "type":"CODE", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "cell_type": "code", + "source": [ + "hub_name = \"klasocki/roberta-large-lora-ner-comma-fixer\"" + ], + "execution_count": 28, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "Dyz7jkJX7CngtBG5SfJB47", + "type": "CODE", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "model.push_to_hub(hub_name)" ], - "execution_count":29, - "outputs":[ + "execution_count": 29, + "outputs": [ { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"2609923ebe3d473eaebc5126eb653fd6" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "2609923ebe3d473eaebc5126eb653fd6" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"c03DMHM7RodIRbeCxJUgzo" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "c03DMHM7RodIRbeCxJUgzo" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "text\/plain":[ - "CommitInfo(commit_url='https:\/\/huggingface.co\/klasocki\/roberta-large-lora-ner-comma-fixer\/commit\/b6e99b176b6814a75e841edcfaa8fef649feaf31', commit_message='Upload model', commit_description='', oid='b6e99b176b6814a75e841edcfaa8fef649feaf31', pr_url=None, pr_revision=None, pr_num=None)" + "data": { + "text/plain": [ + "CommitInfo(commit_url='https://huggingface.co/klasocki/roberta-large-lora-ner-comma-fixer/commit/b6e99b176b6814a75e841edcfaa8fef649feaf31', commit_message='Upload model', commit_description='', oid='b6e99b176b6814a75e841edcfaa8fef649feaf31', pr_url=None, pr_revision=None, pr_num=None)" ] }, - "metadata":{ - - }, - "output_type":"display_data" + "metadata": {}, + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"bvJvzxpWWmNcRzwP5CJpkc", - "type":"CODE", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "metadata": { + "datalore": { + "node_id": "bvJvzxpWWmNcRzwP5CJpkc", + "type": "CODE", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"markdown", - "source":[ + "cell_type": "markdown", + "source": [ "### Inference" ], - "attachments":{ - - }, - "metadata":{ - "datalore":{ - "node_id":"XTrG9hb1fhJ5oV8J3Nm26w", - "type":"MD", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "attachments": {}, + "metadata": { + "datalore": { + "node_id": "XTrG9hb1fhJ5oV8J3Nm26w", + "type": "MD", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "peft_model_id = hub_name\n", "config = PeftConfig.from_pretrained(peft_model_id)\n", "inference_model = AutoModelForTokenClassification.from_pretrained(\n", @@ -2292,82 +2228,80 @@ "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n", "model = PeftModel.from_pretrained(inference_model, peft_model_id)" ], - "execution_count":30, - "outputs":[ + "execution_count": 30, + "outputs": [ { - "name":"stderr", - "text":[ + "name": "stderr", + "text": [ "Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ], - "output_type":"stream" + "output_type": "stream" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"ea85d240073e4115b74c2c2f76a976de" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "ea85d240073e4115b74c2c2f76a976de" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"oRXbz3MAjR4qE4rpRi3lNV" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "oRXbz3MAjR4qE4rpRi3lNV" } } }, - "output_type":"display_data" + "output_type": "display_data" }, { - "data":{ - "application\/vnd.jupyter.widget-view+json":{ - "version_major":2, - "version_minor":0, - "model_id":"6bba9f6fa84a49c5b402fcc40b9c3a20" + "data": { + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "6bba9f6fa84a49c5b402fcc40b9c3a20" } }, - "metadata":{ - "application\/vnd.jupyter.widget-view+json":{ - "datalore":{ - "widget_id":"oOD8zOJnD3w3IRrLQzSxrm" + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "datalore": { + "widget_id": "oOD8zOJnD3w3IRrLQzSxrm" } } }, - "output_type":"display_data" + "output_type": "display_data" } ], - "metadata":{ - "datalore":{ - "node_id":"4Vxwl8BqSlNJ4tt4aQKyu7", - "type":"CODE", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "metadata": { + "datalore": { + "node_id": "4Vxwl8BqSlNJ4tt4aQKyu7", + "type": "CODE", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "text = \"This text should have commas here here and there however it does not.\"\n", "inputs = tokenizer(text, return_tensors=\"pt\")" ], - "execution_count":34, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"vBfrIMnQntSHs406eTmIvN", - "type":"CODE", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "execution_count": 34, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "vBfrIMnQntSHs406eTmIvN", + "type": "CODE", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ + "cell_type": "code", + "source": [ "with torch.no_grad():\n", " logits = model(**inputs).logits\n", "\n", @@ -2377,11 +2311,11 @@ "for token, prediction in zip(tokens, predictions[0].numpy()):\n", " print((token, model.config.id2label[prediction]))" ], - "execution_count":35, - "outputs":[ + "execution_count": 35, + "outputs": [ { - "name":"stdout", - "text":[ + "name": "stdout", + "text": [ "('', 'O')\n", "('This', 'O')\n", "('Ġtext', 'O')\n", @@ -2398,296 +2332,274 @@ "('Ġdoes', 'O')\n", "('Ġnot', 'O')\n", "('.', 'O')\n", - "('<\/s>', 'O')\n" + "('', 'O')\n" ], - "output_type":"stream" + "output_type": "stream" } ], - "metadata":{ - "datalore":{ - "node_id":"jlEMGCI1ZpLhX3dBpF7DrB", - "type":"CODE", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "metadata": { + "datalore": { + "node_id": "jlEMGCI1ZpLhX3dBpF7DrB", + "type": "CODE", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } }, { - "cell_type":"code", - "source":[ - - ], - "execution_count":null, - "outputs":[ - - ], - "metadata":{ - "datalore":{ - "node_id":"pWxG1H7AcqZqzJ543WdvMH", - "type":"CODE", - "hide_input_from_viewers":true, - "hide_output_from_viewers":true + "cell_type": "code", + "source": [], + "execution_count": null, + "outputs": [], + "metadata": { + "datalore": { + "node_id": "pWxG1H7AcqZqzJ543WdvMH", + "type": "CODE", + "hide_input_from_viewers": true, + "hide_output_from_viewers": true } } } ], - "metadata":{ - "widgets":{ - "application\/vnd.jupyter.widget-state+json":{ - "version_major":2, - "version_minor":0, - "state":{ - "1dfa3596c41a47239cd42acaffa03ddc":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "c6b4f1a655334da1976a7bb87d9739a2":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "cd790e68b269453e9ab5b9a961a64279":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_1dfa3596c41a47239cd42acaffa03ddc", - "style":"IPY_MODEL_c6b4f1a655334da1976a7bb87d9739a2", - "value":"Token is valid (permission: write)." - } - }, - "b6f37cc798714f57b51bcd8284fe5cf3":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "5fab119f434047038d02b468858f9565":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "81734d38280d4abdb9acc5295c143cc9":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_b6f37cc798714f57b51bcd8284fe5cf3", - "style":"IPY_MODEL_5fab119f434047038d02b468858f9565", - "value":"\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine." - } - }, - "22220074fffd4176b02ec067dd3b5ae8":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "c4ab8887bd204069bb783ff6e8ae7723":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "43ba6d98d5a849188b48919aa6494396":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_22220074fffd4176b02ec067dd3b5ae8", - "style":"IPY_MODEL_c4ab8887bd204069bb783ff6e8ae7723", - "value":"You might have to re-authenticate when pushing to the Hugging Face Hub." - } - }, - "e0a3c0f366ba468e9cb4aded19c802b4":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "2bbcd517c13a47fb88dd1ec65120abd1":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "ce593c77612d4e7395f6a9b880f84460":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_e0a3c0f366ba468e9cb4aded19c802b4", - "style":"IPY_MODEL_2bbcd517c13a47fb88dd1ec65120abd1", - "value":"Run the following command in your terminal in case you want to set the 'store' credential helper as default." - } - }, - "53609e0450164573a5604a184e504349":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "96288508db244ae390d26c1cf739ff73":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "242cefc7ee344b64ae38e8d17d756437":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_53609e0450164573a5604a184e504349", - "style":"IPY_MODEL_96288508db244ae390d26c1cf739ff73", - "value":"git config --global credential.helper store" - } - }, - "10df0fb54e6c40e7bfd704e051792c5c":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "1b4886b66e8a4041b3782bc1387e8ec8":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "c27034c532f9404fb5319286abdeb0a8":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_10df0fb54e6c40e7bfd704e051792c5c", - "style":"IPY_MODEL_1b4886b66e8a4041b3782bc1387e8ec8", - "value":"Read https:\/\/git-scm.com\/book\/en\/v2\/Git-Tools-Credential-Storage for more details.\u001b[0m" - } - }, - "7c0fd075376144b790c33685d4c71e31":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "4ed78afe8cfe41d4be4204980b287005":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "32b669c3e94547d984bd0cddfef27b47":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_7c0fd075376144b790c33685d4c71e31", - "style":"IPY_MODEL_4ed78afe8cfe41d4be4204980b287005", - "value":"Token has not been saved to git credential helper." - } - }, - "9386980fa9684653a205701b24f4c3d7":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "44aadab07124477781bcb65fe9e681ba":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "15ddc7ac585c49ae8aae232b176ddc7d":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_9386980fa9684653a205701b24f4c3d7", - "style":"IPY_MODEL_44aadab07124477781bcb65fe9e681ba", - "value":"Your token has been saved to \/home\/datalore\/.cache\/huggingface\/token" - } - }, - "41e8c5786b2041b3b34410041bc9f88f":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "f949e7e6496747b396e00d0a23de8b64":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "74e374e308fe451ba997207ca19244f3":{ - "model_name":"LabelModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_41e8c5786b2041b3b34410041bc9f88f", - "style":"IPY_MODEL_f949e7e6496747b396e00d0a23de8b64", - "value":"Login successful" - } - }, - "20fc5c16124b4e6a805073c5de94b74a":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - "align_items":"center", - "display":"flex", - "flex_flow":"column", - "width":"50%" - } - }, - "aee257c0976d425da20f443a3973cf36":{ - "model_name":"VBoxModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "children":[ + "metadata": { + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "1dfa3596c41a47239cd42acaffa03ddc": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "c6b4f1a655334da1976a7bb87d9739a2": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "cd790e68b269453e9ab5b9a961a64279": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_1dfa3596c41a47239cd42acaffa03ddc", + "style": "IPY_MODEL_c6b4f1a655334da1976a7bb87d9739a2", + "value": "Token is valid (permission: write)." + } + }, + "b6f37cc798714f57b51bcd8284fe5cf3": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "5fab119f434047038d02b468858f9565": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "81734d38280d4abdb9acc5295c143cc9": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_b6f37cc798714f57b51bcd8284fe5cf3", + "style": "IPY_MODEL_5fab119f434047038d02b468858f9565", + "value": "\u001B[1m\u001B[31mCannot authenticate through git-credential as no helper is defined on your machine." + } + }, + "22220074fffd4176b02ec067dd3b5ae8": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "c4ab8887bd204069bb783ff6e8ae7723": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "43ba6d98d5a849188b48919aa6494396": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_22220074fffd4176b02ec067dd3b5ae8", + "style": "IPY_MODEL_c4ab8887bd204069bb783ff6e8ae7723", + "value": "You might have to re-authenticate when pushing to the Hugging Face Hub." + } + }, + "e0a3c0f366ba468e9cb4aded19c802b4": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "2bbcd517c13a47fb88dd1ec65120abd1": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "ce593c77612d4e7395f6a9b880f84460": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_e0a3c0f366ba468e9cb4aded19c802b4", + "style": "IPY_MODEL_2bbcd517c13a47fb88dd1ec65120abd1", + "value": "Run the following command in your terminal in case you want to set the 'store' credential helper as default." + } + }, + "53609e0450164573a5604a184e504349": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "96288508db244ae390d26c1cf739ff73": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "242cefc7ee344b64ae38e8d17d756437": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_53609e0450164573a5604a184e504349", + "style": "IPY_MODEL_96288508db244ae390d26c1cf739ff73", + "value": "git config --global credential.helper store" + } + }, + "10df0fb54e6c40e7bfd704e051792c5c": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "1b4886b66e8a4041b3782bc1387e8ec8": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "c27034c532f9404fb5319286abdeb0a8": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_10df0fb54e6c40e7bfd704e051792c5c", + "style": "IPY_MODEL_1b4886b66e8a4041b3782bc1387e8ec8", + "value": "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001B[0m" + } + }, + "7c0fd075376144b790c33685d4c71e31": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "4ed78afe8cfe41d4be4204980b287005": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "32b669c3e94547d984bd0cddfef27b47": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_7c0fd075376144b790c33685d4c71e31", + "style": "IPY_MODEL_4ed78afe8cfe41d4be4204980b287005", + "value": "Token has not been saved to git credential helper." + } + }, + "9386980fa9684653a205701b24f4c3d7": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "44aadab07124477781bcb65fe9e681ba": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "15ddc7ac585c49ae8aae232b176ddc7d": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_9386980fa9684653a205701b24f4c3d7", + "style": "IPY_MODEL_44aadab07124477781bcb65fe9e681ba", + "value": "Your token has been saved to /home/datalore/.cache/huggingface/token" + } + }, + "41e8c5786b2041b3b34410041bc9f88f": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "f949e7e6496747b396e00d0a23de8b64": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "74e374e308fe451ba997207ca19244f3": { + "model_name": "LabelModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_41e8c5786b2041b3b34410041bc9f88f", + "style": "IPY_MODEL_f949e7e6496747b396e00d0a23de8b64", + "value": "Login successful" + } + }, + "20fc5c16124b4e6a805073c5de94b74a": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": { + "align_items": "center", + "display": "flex", + "flex_flow": "column", + "width": "50%" + } + }, + "aee257c0976d425da20f443a3973cf36": { + "model_name": "VBoxModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "children": [ "IPY_MODEL_cd790e68b269453e9ab5b9a961a64279", "IPY_MODEL_81734d38280d4abdb9acc5295c143cc9", "IPY_MODEL_43ba6d98d5a849188b48919aa6494396", @@ -2698,328 +2610,302 @@ "IPY_MODEL_15ddc7ac585c49ae8aae232b176ddc7d", "IPY_MODEL_74e374e308fe451ba997207ca19244f3" ], - "layout":"IPY_MODEL_20fc5c16124b4e6a805073c5de94b74a" - } - }, - "ba6b997bc4f749368bcf3c7d75e59dd1":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "b53ebb5e12084d9fa25195fc22715ac8":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "9a00dc77ccb844faa7a7f66a2f58b942":{ - "model_name":"HTMLModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_ba6b997bc4f749368bcf3c7d75e59dd1", - "style":"IPY_MODEL_b53ebb5e12084d9fa25195fc22715ac8", - "value":"adapter_model.bin: 100%" - } - }, - "eb326383bb0f4da9935c37eb85073a40":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "6c4558d629c34e689df32a021745ead8":{ - "model_name":"ProgressStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "791c2cbe9ba643139fbc4119efeed65a":{ - "model_name":"FloatProgressModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "bar_style":"success", - "layout":"IPY_MODEL_eb326383bb0f4da9935c37eb85073a40", - "max":7490973, - "style":"IPY_MODEL_6c4558d629c34e689df32a021745ead8", - "value":7490973 - } - }, - "3b922c305a7d48bab391317cd657f8d5":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "1440148cee1e4c8486a2abebbc7faa23":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "e6529cb132894cfab125b482198822b8":{ - "model_name":"HTMLModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_3b922c305a7d48bab391317cd657f8d5", - "style":"IPY_MODEL_1440148cee1e4c8486a2abebbc7faa23", - "value":" 7.49M\/7.49M [00:04<00:00, 2.26MB\/s]" - } - }, - "872fe87fd82c40a89442998ff471b881":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "2609923ebe3d473eaebc5126eb653fd6":{ - "model_name":"HBoxModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "children":[ + "layout": "IPY_MODEL_20fc5c16124b4e6a805073c5de94b74a" + } + }, + "ba6b997bc4f749368bcf3c7d75e59dd1": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "b53ebb5e12084d9fa25195fc22715ac8": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "9a00dc77ccb844faa7a7f66a2f58b942": { + "model_name": "HTMLModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_ba6b997bc4f749368bcf3c7d75e59dd1", + "style": "IPY_MODEL_b53ebb5e12084d9fa25195fc22715ac8", + "value": "adapter_model.bin: 100%" + } + }, + "eb326383bb0f4da9935c37eb85073a40": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "6c4558d629c34e689df32a021745ead8": { + "model_name": "ProgressStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "791c2cbe9ba643139fbc4119efeed65a": { + "model_name": "FloatProgressModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "bar_style": "success", + "layout": "IPY_MODEL_eb326383bb0f4da9935c37eb85073a40", + "max": 7490973, + "style": "IPY_MODEL_6c4558d629c34e689df32a021745ead8", + "value": 7490973 + } + }, + "3b922c305a7d48bab391317cd657f8d5": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "1440148cee1e4c8486a2abebbc7faa23": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "e6529cb132894cfab125b482198822b8": { + "model_name": "HTMLModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_3b922c305a7d48bab391317cd657f8d5", + "style": "IPY_MODEL_1440148cee1e4c8486a2abebbc7faa23", + "value": " 7.49M/7.49M [00:04<00:00, 2.26MB/s]" + } + }, + "872fe87fd82c40a89442998ff471b881": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "2609923ebe3d473eaebc5126eb653fd6": { + "model_name": "HBoxModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "children": [ "IPY_MODEL_9a00dc77ccb844faa7a7f66a2f58b942", "IPY_MODEL_791c2cbe9ba643139fbc4119efeed65a", "IPY_MODEL_e6529cb132894cfab125b482198822b8" ], - "layout":"IPY_MODEL_872fe87fd82c40a89442998ff471b881" - } - }, - "335387903ecb43a586d049bed0fbc012":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "1837a2056268420cb2d90e740c606892":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "e1ee514268c34c298c2641d22457816c":{ - "model_name":"HTMLModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_335387903ecb43a586d049bed0fbc012", - "style":"IPY_MODEL_1837a2056268420cb2d90e740c606892", - "value":"Downloading (…)\/adapter_config.json: 100%" - } - }, - "5c843a2afc054e7ab2531cc530da226a":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "f42fd48e69eb43188397bd5a2cda9a30":{ - "model_name":"ProgressStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "8cb66793a8d54b1ab3ecc65157bcd6a9":{ - "model_name":"FloatProgressModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "bar_style":"success", - "layout":"IPY_MODEL_5c843a2afc054e7ab2531cc530da226a", - "max":432, - "style":"IPY_MODEL_f42fd48e69eb43188397bd5a2cda9a30", - "value":432 - } - }, - "75fdf0dad626469ab2d138711d72cf08":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "f21e9954b48c4ee288f3782755daaf1e":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "2580697cd74a4e5d8024a7a1e7ad114c":{ - "model_name":"HTMLModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_75fdf0dad626469ab2d138711d72cf08", - "style":"IPY_MODEL_f21e9954b48c4ee288f3782755daaf1e", - "value":" 432\/432 [00:00<00:00, 28.6kB\/s]" - } - }, - "d2f6e2c8392e4f18ac06dd3d0de6acf1":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "ea85d240073e4115b74c2c2f76a976de":{ - "model_name":"HBoxModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "children":[ + "layout": "IPY_MODEL_872fe87fd82c40a89442998ff471b881" + } + }, + "335387903ecb43a586d049bed0fbc012": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "1837a2056268420cb2d90e740c606892": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "e1ee514268c34c298c2641d22457816c": { + "model_name": "HTMLModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_335387903ecb43a586d049bed0fbc012", + "style": "IPY_MODEL_1837a2056268420cb2d90e740c606892", + "value": "Downloading (…)/adapter_config.json: 100%" + } + }, + "5c843a2afc054e7ab2531cc530da226a": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "f42fd48e69eb43188397bd5a2cda9a30": { + "model_name": "ProgressStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "8cb66793a8d54b1ab3ecc65157bcd6a9": { + "model_name": "FloatProgressModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "bar_style": "success", + "layout": "IPY_MODEL_5c843a2afc054e7ab2531cc530da226a", + "max": 432, + "style": "IPY_MODEL_f42fd48e69eb43188397bd5a2cda9a30", + "value": 432 + } + }, + "75fdf0dad626469ab2d138711d72cf08": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "f21e9954b48c4ee288f3782755daaf1e": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "2580697cd74a4e5d8024a7a1e7ad114c": { + "model_name": "HTMLModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_75fdf0dad626469ab2d138711d72cf08", + "style": "IPY_MODEL_f21e9954b48c4ee288f3782755daaf1e", + "value": " 432/432 [00:00<00:00, 28.6kB/s]" + } + }, + "d2f6e2c8392e4f18ac06dd3d0de6acf1": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "ea85d240073e4115b74c2c2f76a976de": { + "model_name": "HBoxModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "children": [ "IPY_MODEL_e1ee514268c34c298c2641d22457816c", "IPY_MODEL_8cb66793a8d54b1ab3ecc65157bcd6a9", "IPY_MODEL_2580697cd74a4e5d8024a7a1e7ad114c" ], - "layout":"IPY_MODEL_d2f6e2c8392e4f18ac06dd3d0de6acf1" - } - }, - "026ed0e099e04df2864a36ca00c8a695":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "80cede5c3d1d44fc80b1e87d60c4d200":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "34ad5085cda04eff80e9b5314be98c41":{ - "model_name":"HTMLModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_026ed0e099e04df2864a36ca00c8a695", - "style":"IPY_MODEL_80cede5c3d1d44fc80b1e87d60c4d200", - "value":"Downloading adapter_model.bin: 100%" - } - }, - "1ec286d338ec4457980391951d3cc025":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "5d0e68b8382645bcb9da308ecf025089":{ - "model_name":"ProgressStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "b0f2185867c94543a4491b0ecb047bc4":{ - "model_name":"FloatProgressModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "bar_style":"success", - "layout":"IPY_MODEL_1ec286d338ec4457980391951d3cc025", - "max":7490973, - "style":"IPY_MODEL_5d0e68b8382645bcb9da308ecf025089", - "value":7490973 - } - }, - "7d5e8608d98441328ec9187e23ea34a4":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "bb5b3e305f9d409cb0916be9ec52d921":{ - "model_name":"DescriptionStyleModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "description_width":"" - } - }, - "e619f8cac6b04b1dbe7075c74f8e37fc":{ - "model_name":"HTMLModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "layout":"IPY_MODEL_7d5e8608d98441328ec9187e23ea34a4", - "style":"IPY_MODEL_bb5b3e305f9d409cb0916be9ec52d921", - "value":" 7.49M\/7.49M [00:00<00:00, 13.5MB\/s]" - } - }, - "c0bc4d0adacc4f75b1341c772811a50a":{ - "model_name":"LayoutModel", - "model_module":"@jupyter-widgets\/base", - "model_module_version":"1.2.0", - "state":{ - - } - }, - "6bba9f6fa84a49c5b402fcc40b9c3a20":{ - "model_name":"HBoxModel", - "model_module":"@jupyter-widgets\/controls", - "model_module_version":"1.5.0", - "state":{ - "children":[ + "layout": "IPY_MODEL_d2f6e2c8392e4f18ac06dd3d0de6acf1" + } + }, + "026ed0e099e04df2864a36ca00c8a695": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "80cede5c3d1d44fc80b1e87d60c4d200": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "34ad5085cda04eff80e9b5314be98c41": { + "model_name": "HTMLModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_026ed0e099e04df2864a36ca00c8a695", + "style": "IPY_MODEL_80cede5c3d1d44fc80b1e87d60c4d200", + "value": "Downloading adapter_model.bin: 100%" + } + }, + "1ec286d338ec4457980391951d3cc025": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "5d0e68b8382645bcb9da308ecf025089": { + "model_name": "ProgressStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "b0f2185867c94543a4491b0ecb047bc4": { + "model_name": "FloatProgressModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "bar_style": "success", + "layout": "IPY_MODEL_1ec286d338ec4457980391951d3cc025", + "max": 7490973, + "style": "IPY_MODEL_5d0e68b8382645bcb9da308ecf025089", + "value": 7490973 + } + }, + "7d5e8608d98441328ec9187e23ea34a4": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "bb5b3e305f9d409cb0916be9ec52d921": { + "model_name": "DescriptionStyleModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "description_width": "" + } + }, + "e619f8cac6b04b1dbe7075c74f8e37fc": { + "model_name": "HTMLModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "layout": "IPY_MODEL_7d5e8608d98441328ec9187e23ea34a4", + "style": "IPY_MODEL_bb5b3e305f9d409cb0916be9ec52d921", + "value": " 7.49M/7.49M [00:00<00:00, 13.5MB/s]" + } + }, + "c0bc4d0adacc4f75b1341c772811a50a": { + "model_name": "LayoutModel", + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "state": {} + }, + "6bba9f6fa84a49c5b402fcc40b9c3a20": { + "model_name": "HBoxModel", + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "state": { + "children": [ "IPY_MODEL_34ad5085cda04eff80e9b5314be98c41", "IPY_MODEL_b0f2185867c94543a4491b0ecb047bc4", "IPY_MODEL_e619f8cac6b04b1dbe7075c74f8e37fc" ], - "layout":"IPY_MODEL_c0bc4d0adacc4f75b1341c772811a50a" + "layout": "IPY_MODEL_c0bc4d0adacc4f75b1341c772811a50a" } } } } }, - "kernelspec":{ - "display_name":"Python", - "language":"python", - "name":"python" + "kernelspec": { + "display_name": "Python", + "language": "python", + "name": "python" }, - "datalore":{ - "computation_mode":"JUPYTER", - "package_manager":"pip", - "base_environment":"default", - "packages":[ - - ], - "report_row_ids":[ + "datalore": { + "computation_mode": "JUPYTER", + "package_manager": "pip", + "base_environment": "default", + "packages": [], + "report_row_ids": [ "9tLzdz03okTFWD3Vy0h4vS", "h8AdYv2Y3tw7Yb4W3IGoxc", "sGTotT7rOtZWKTPgVm4gX6", @@ -3045,9 +2931,9 @@ "sgKkzWyaxUFUAVeYMkmETq", "pMxR5mn4hMefEjvfv5e39e" ], - "version":3 + "version": 3 } }, - "nbformat":4, - "nbformat_minor":4 -} \ No newline at end of file + "nbformat": 4, + "nbformat_minor": 4 +}