diff --git "a/IA3.ipynb" "b/IA3.ipynb"
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+++ "b/IA3.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "a9935ae2",
+ "metadata": {
+ "id": "a9935ae2"
+ },
+ "outputs": [],
+ "source": [
+ "import argparse\n",
+ "import os\n",
+ "\n",
+ "import torch\n",
+ "from torch.optim import AdamW\n",
+ "from torch.utils.data import DataLoader\n",
+ "import peft\n",
+ "\n",
+ "import evaluate\n",
+ "from datasets import load_dataset\n",
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e3b13308",
+ "metadata": {
+ "id": "e3b13308"
+ },
+ "outputs": [],
+ "source": [
+ "batch_size = 8\n",
+ "model_name_or_path = \"roberta-large\"\n",
+ "task = \"mrpc\"\n",
+ "peft_type = peft.PeftType.IA3\n",
+ "device = \"cuda\"\n",
+ "num_epochs = 12"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0526f571",
+ "metadata": {
+ "id": "0526f571"
+ },
+ "outputs": [],
+ "source": [
+ "# peft_config = LoraConfig(task_type=\"SEQ_CLS\", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1)\n",
+ "peft_config = peft.IA3Config(task_type=\"SEQ_CLS\", inference_mode=False)\n",
+ "lr = 1e-3"
+ ]
+ },
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+ "model_id": "007f2f512694405c9245edd5e1f58551"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/3 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "6b937f0053b64c67af74d44bcb6e51b7"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading builder script: 0%| | 0.00/5.75k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "75ee099566ca46eb8a75475246aaab01"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/3668 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a018f7c156584eb3833b6aba3710b3c0"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/408 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f871eeaa54cf451b8a2de64eed90d5b6"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/1725 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e41888b1dea140d18be25efc6d99d0c3"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
+ " padding_side = \"left\"\n",
+ "else:\n",
+ " padding_side = \"right\"\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
+ "if getattr(tokenizer, \"pad_token_id\") is None:\n",
+ " tokenizer.pad_token_id = tokenizer.eos_token_id\n",
+ "\n",
+ "datasets = load_dataset(\"glue\", task)\n",
+ "metric = evaluate.load(\"glue\", task)\n",
+ "\n",
+ "\n",
+ "def tokenize_function(examples):\n",
+ " # max_length=None => use the model max length (it's actually the default)\n",
+ " outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
+ " return outputs\n",
+ "\n",
+ "\n",
+ "tokenized_datasets = datasets.map(\n",
+ " tokenize_function,\n",
+ " batched=True,\n",
+ " remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
+ ")\n",
+ "\n",
+ "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
+ "# transformers library\n",
+ "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
+ "\n",
+ "\n",
+ "def collate_fn(examples):\n",
+ " return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
+ "\n",
+ "\n",
+ "# Instantiate dataloaders.\n",
+ "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
+ "eval_dataloader = DataLoader(\n",
+ " tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
+ ")\n",
+ "test_dataloader = DataLoader(tokenized_datasets[\"test\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "2ed5ac74",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "0cecb897c86c4892b94a1990ab08a926",
+ "b8af0294819e4280ad41fa1c11006adf",
+ "c7530d63b2f745e799713284abacbd2c",
+ "12a1e302a69543c5bc0e0a66be008ca0",
+ "9c372e9e9b20433faed8530ca0f4424c",
+ "b07f26a21325493cac19113f1aa1ee96",
+ "fbdf6c544fb54294903524a69384e773",
+ "6c6f2223243b4a7485aef7fbbfe07668",
+ "a93509d61ac94628a74bc0f98c0eec06",
+ "3a8de0eb7db44647a734590b6b351b44",
+ "64d8affd2e854a1c9043fec7ca8a2796"
+ ]
+ },
+ "id": "2ed5ac74",
+ "outputId": "18ea15ac-ed8d-4d80-b166-706681ee49ab"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading model.safetensors: 0%| | 0.00/1.42G [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0cecb897c86c4892b94a1990ab08a926"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
+ "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias']\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",
+ "name": "stdout",
+ "text": [
+ "trainable params: 2,275,332 || all params: 356,585,476 || trainable%: 0.6380888042675075\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "PeftModelForSequenceClassification(\n",
+ " (base_model): IA3Model(\n",
+ " (model): RobertaForSequenceClassification(\n",
+ " (roberta): RobertaModel(\n",
+ " (embeddings): RobertaEmbeddings(\n",
+ " (word_embeddings): Embedding(50265, 1024, padding_idx=1)\n",
+ " (position_embeddings): Embedding(514, 1024, padding_idx=1)\n",
+ " (token_type_embeddings): Embedding(1, 1024)\n",
+ " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): RobertaEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0-23): 24 x RobertaLayer(\n",
+ " (attention): RobertaAttention(\n",
+ " (self): RobertaSelfAttention(\n",
+ " (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
+ " (key): Linear(\n",
+ " in_features=1024, out_features=1024, bias=True\n",
+ " (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
+ " )\n",
+ " (value): Linear(\n",
+ " in_features=1024, out_features=1024, bias=True\n",
+ " (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
+ " )\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (output): RobertaSelfOutput(\n",
+ " (dense): Linear(\n",
+ " in_features=1024, out_features=1024, bias=True\n",
+ " (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1x1024])\n",
+ " )\n",
+ " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): RobertaIntermediate(\n",
+ " (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): RobertaOutput(\n",
+ " (dense): Linear(\n",
+ " in_features=4096, out_features=1024, bias=True\n",
+ " (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1x4096])\n",
+ " )\n",
+ " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (classifier): ModulesToSaveWrapper(\n",
+ " (original_module): RobertaClassificationHead(\n",
+ " (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " (out_proj): Linear(in_features=1024, out_features=2, bias=True)\n",
+ " )\n",
+ " (modules_to_save): ModuleDict(\n",
+ " (default): RobertaClassificationHead(\n",
+ " (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " (out_proj): Linear(in_features=1024, out_features=2, bias=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 6
+ }
+ ],
+ "source": [
+ "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
+ "model = peft.get_peft_model(model, peft_config)\n",
+ "model.print_trainable_parameters()\n",
+ "model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "0d2d0381",
+ "metadata": {
+ "id": "0d2d0381"
+ },
+ "outputs": [],
+ "source": [
+ "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
+ "\n",
+ "# Instantiate scheduler\n",
+ "lr_scheduler = get_linear_schedule_with_warmup(\n",
+ " optimizer=optimizer,\n",
+ " num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
+ " num_training_steps=(len(train_dataloader) * num_epochs),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "fa0e73be",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fa0e73be",
+ "outputId": "bb17c146-8acc-477d-8f9f-65b8be794abb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ " 0%| | 0/459 [00:00<?, ?it/s]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",
+ "100%|██████████| 459/459 [01:41<00:00, 4.52it/s]\n",
+ "100%|██████████| 51/51 [00:05<00:00, 8.89it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 0: {'accuracy': 0.7034313725490197, 'f1': 0.8212703101920238}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:45<00:00, 4.35it/s]\n",
+ "100%|██████████| 51/51 [00:05<00:00, 8.66it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 1: {'accuracy': 0.7794117647058824, 'f1': 0.8432055749128919}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:47<00:00, 4.26it/s]\n",
+ "100%|██████████| 51/51 [00:05<00:00, 8.50it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 2: {'accuracy': 0.8406862745098039, 'f1': 0.8794063079777366}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:49<00:00, 4.20it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 8.45it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 3: {'accuracy': 0.8480392156862745, 'f1': 0.8923611111111109}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:49<00:00, 4.18it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 7.52it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 4: {'accuracy': 0.8529411764705882, 'f1': 0.894736842105263}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:49<00:00, 4.19it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 8.47it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 5: {'accuracy': 0.8700980392156863, 'f1': 0.9090909090909091}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██���███████| 459/459 [01:49<00:00, 4.20it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 8.41it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 6: {'accuracy': 0.875, 'f1': 0.9090909090909091}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:49<00:00, 4.21it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 8.41it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 7: {'accuracy': 0.8676470588235294, 'f1': 0.9042553191489361}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:50<00:00, 4.17it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 8.45it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 8: {'accuracy': 0.8700980392156863, 'f1': 0.9068541300527241}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:49<00:00, 4.20it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 8.39it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 9: {'accuracy': 0.8676470588235294, 'f1': 0.9028776978417268}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:49<00:00, 4.20it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 8.39it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 10: {'accuracy': 0.875, 'f1': 0.9100529100529101}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 459/459 [01:49<00:00, 4.18it/s]\n",
+ "100%|██████████| 51/51 [00:06<00:00, 8.41it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 11: {'accuracy': 0.8651960784313726, 'f1': 0.9012567324955117}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.to(device)\n",
+ "for epoch in range(num_epochs):\n",
+ " model.train()\n",
+ " for step, batch in enumerate(tqdm(train_dataloader)):\n",
+ " batch.to(device)\n",
+ " outputs = model(**batch)\n",
+ " loss = outputs.loss\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " lr_scheduler.step()\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " model.eval()\n",
+ " for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+ " batch.to(device)\n",
+ " with torch.no_grad():\n",
+ " outputs = model(**batch)\n",
+ " predictions = outputs.logits.argmax(dim=-1)\n",
+ " predictions, references = predictions, batch[\"labels\"]\n",
+ " metric.add_batch(\n",
+ " predictions=predictions,\n",
+ " references=references,\n",
+ " )\n",
+ "\n",
+ " eval_metric = metric.compute()\n",
+ " print(f\"epoch {epoch}:\", eval_metric)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f2b2caca",
+ "metadata": {
+ "id": "f2b2caca"
+ },
+ "source": [
+ "## Share adapters on the 🤗 Hub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "990b3c93",
+ "metadata": {
+ "colab": {
+ "referenced_widgets": [
+ "ad8979af959541b2a5a67f389884f057",
+ "1c020005cfe04de6bdd404ef41e0dfef"
+ ]
+ },
+ "id": "990b3c93",
+ "outputId": "0e2f13c2-0285-4f47-ab9a-d3aefea7ee05"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ad8979af959541b2a5a67f389884f057",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1c020005cfe04de6bdd404ef41e0dfef",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_model.bin: 0%| | 0.00/4.93M [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/SumanthRH/roberta-large-peft-ia3/commit/9968de770e53ecd24e8e689a10144b7f55059a75', commit_message='Upload model', commit_description='', oid='9968de770e53ecd24e8e689a10144b7f55059a75', pr_url='https://huggingface.co/SumanthRH/roberta-large-peft-ia3/discussions/3', pr_revision='refs/pr/3', pr_num=3)"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.push_to_hub(\"SumanthRH/roberta-large-peft-ia3\", use_auth_token=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d140b26",
+ "metadata": {
+ "id": "9d140b26"
+ },
+ "source": [
+ "## Load adapters from the Hub\n",
+ "\n",
+ "You can also directly load adapters from the Hub using the commands below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4d55c87d",
+ "metadata": {
+ "colab": {
+ "referenced_widgets": [
+ "fd320d7b5d9a43df8abf044897e25cc7",
+ "0022338db24d43bba27d0e72773855e0"
+ ]
+ },
+ "id": "4d55c87d",
+ "outputId": "e6e232d7-22a3-4c85-c72e-90d8275c9762"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading (…)/adapter_config.json: 0%| | 0.00/345 [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.decoder.weight', 'roberta.pooler.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.bias']\n",
+ "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ },
+ {
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+ "Downloading adapter_model.bin: 0%| | 0.00/4.93M [00:00<?, ?B/s]"
+ ]
+ },
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+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 0%| | 0/51 [00:00<?, ?it/s]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",
+ "100%|████████████████████████████████████████████████████████████████████████��████████████████████████████████████████████████████████████████████████████████| 51/51 [00:04<00:00, 10.57it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'accuracy': 0.8774509803921569, 'f1': 0.9116607773851589}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from peft import PeftModel, PeftConfig\n",
+ "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+ "\n",
+ "peft_model_id = \"SumanthRH/roberta-large-peft-ia3\"\n",
+ "config = PeftConfig.from_pretrained(peft_model_id)\n",
+ "inference_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+ "\n",
+ "# Load the Lora model\n",
+ "inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
+ "\n",
+ "inference_model.to(device)\n",
+ "inference_model.eval()\n",
+ "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+ " batch.to(device)\n",
+ " with torch.no_grad():\n",
+ " outputs = inference_model(**batch)\n",
+ " predictions = outputs.logits.argmax(dim=-1)\n",
+ " predictions, references = predictions, batch[\"labels\"]\n",
+ " metric.add_batch(\n",
+ " predictions=predictions,\n",
+ " references=references,\n",
+ " )\n",
+ "\n",
+ "eval_metric = metric.compute()\n",
+ "print(eval_metric)"
+ ]
+ },
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