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"e914d47da6534546b2f81ef3466e0ebe", "7230eb392eb647bd97652afd53c6ebb4", "997f9d75329b462ab48257c43debf288", "5827f8a0f5d04421acd58fef38d6d5dd", "b21a138462be41199bddc6206dd82606", "4148904ba96d4696bd61afed0d46208b", "d32e1b61468f4195ae3a9a98f87835ec", "cc36d3899f6445b6b1d42065f03c2b28", "21e3ff105cb94808bd6e18023fb66f28", "6f828a0a0b56478c96cc543dc030b120", "85efe5d529aa45b29376993ef327ada8", "58e80e1138794c9e88d0b49f60e5bf18", "80557b4db2cd4569981d4bf4be9fa843", "d020c857d73c432a81c5fce006a6a36f", "c22dd0487a6a4b4385977e87ca8b10aa", "cdc45f8a3dca4f8e91f784178b17e0f5", "ddb2f51fbd764e04b5fd8fd79770b33e", "106e5b2bde684de1ab4a57dda00789f4", "bec001856789461aa3c7478ba62f3a02", "73a0b77cf8004478b026c27c3c3f8619", "5d085cfdb0194ffc8a45dec89e15e780", "accd2b4f89644e81a1a665167e3c5705", "3a24897b5a634cfaa3b786dd2d9b70ce", "cdb3131ef3a946b7a5365076c1149bfd", "1d3056609cbf40c0aabc0cd9c9a8433f", "8102b559ae96466f9cc64a95fa0bebfd", "d68cb276e38b4c33b94d4756743a7a5e", "1fd8bdfad8b441a4809cf431c3244093", "4c9d456bfbb2406a927d25f938d81109", "3877d0e4c8ca46f88a0e2f34c2398371", "8a5dd0c4314c427ea7320a75e3796e9d", "f98cdf54a8b94e7a93715ff5cfa286de", "6c5020671b984e6a8012b35bd2dffd8f", "fdc21758404b42fab913402933c0e7cc" ] }, "outputId": "a9309a63-2e19-46b6-9bbb-58fab357bf1c" }, "execution_count": 7, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ddb604f198c4406aa3a2af05ffa83956", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0%| | 0.00/8.48k [00:00 tuple[float, float]:\n", " \"\"\"\n", " Evaluates predicted texts against ground truth texts in terms of comma placement.\n", " For each comma from the predicted text:\n", " If it should be there, according to ground truth, it counts as a true positive.\n", " If it should not be there, it counts as a false positive.\n", " If a comma from ground truth is not predicted, it counts as a false negative.\n", " :param gt_texts: Ground truth texts.\n", " :param pred_texts: Predicted texts.\n", " :return: Prints out the classification report, and returns the precision and recall scores.\n", " \"\"\"\n", " def _seqeval_tags(normalized_text: str) -> list[str]:\n", " return ['B-COMMA' if token.endswith('-COMMA') else 'O' for token in normalized_text.split()]\n", " # Replacing commas with suffixes for the preceding token is a hack to make sure\n", " # number of tokens is equal between ground truth and predicted. It allows us\n", " # to convert texts into sequences of tags, and use seqeval metrics.\n", " tags_gt = [_seqeval_tags(gt.replace(' , ', '-COMMA ')) for gt in gt_texts]\n", " tags_pred = [_seqeval_tags(pred.replace(', ', '-COMMA ')) for pred in pred_texts]\n", "\n", " print(classification_report(y_true=tags_gt, y_pred=tags_pred))\n", " return precision_score(y_true=tags_gt, y_pred=tags_pred), recall_score(y_true=tags_gt, y_pred=tags_pred)" ], "metadata": { "id": "6Ow47ROcPD1Q" }, "execution_count": 19, "outputs": [] }, { "cell_type": "code", "source": [ "assert evaluate([gt], [pred]) == (0.75, 0.6)\n", "assert evaluate([gt], [baseline_pred]) == (1.0, 0.4)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SVuOoTWxUvWm", "outputId": "226a454f-8e67-4b6b-b7dc-928cb7cf60cb" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " COMMA 0.75 0.60 0.67 5\n", "\n", " micro avg 0.75 0.60 0.67 5\n", " macro avg 0.75 0.60 0.67 5\n", "weighted avg 0.75 0.60 0.67 5\n", "\n", " precision recall f1-score support\n", "\n", " COMMA 1.00 0.40 0.57 5\n", "\n", " micro avg 1.00 0.40 0.57 5\n", " macro avg 1.00 0.40 0.57 5\n", "weighted avg 1.00 0.40 0.57 5\n" ] } ] }, { "cell_type": "markdown", "source": [ "#### Prepare texts for evaluation" ], "metadata": { "id": "QZSBcn0MqHEU" } }, { "cell_type": "code", "source": [ "gt_texts = [x[\"text\"] for x in wikitext]" ], "metadata": { "id": "7nNHOQsXUynZ" }, "execution_count": 25, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Baseline evaluation" ], "metadata": { "id": "P5VL39-aGwdI" } }, { "cell_type": "markdown", "source": [ "To make sure we are utilizing the GPU properly, we operate directly on the dataset using the pipeline as opposed to our custom class." ], "metadata": { "id": "IMP_MpcQqNSb" } }, { "cell_type": "code", "source": [ "from tqdm import tqdm\n", "from transformers.pipelines.pt_utils import KeyDataset\n", "from commafixer.src.baseline import _remove_punctuation, _fix_commas_based_on_pipeline_output" ], "metadata": { "id": "t2pdpXIkXgm6" }, "execution_count": 26, "outputs": [] }, { "cell_type": "markdown", "source": [ "Preparing the dataset with removed punctuation, needed for the baseline model." ], "metadata": { "id": "IJeCBPudqYS3" } }, { "cell_type": "code", "source": [ "def map_function(text) -> dict:\n", " t, punctuation_indices = _remove_punctuation(text[\"text\"])\n", " return {\"text\": t, \"indices\": punctuation_indices}\n", "\n", "wikitext_no_punct = wikitext.map(map_function)\n", "wikitext_no_punct[3]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 171, "referenced_widgets": [ "3bbf16882e7849c1a27b31978beb806c", "7d41e2f8dd36498b992528998d6d8755", "efb47aaa71474d0d97369a24c31cfce3", "549565c7c531400a96835ed84f13ffef", "0355866aaaa64658917b5461d048c855", "89d6d50ae8024360b58afde8af4f0494", "2c048200d5c94edeb50a3c4e802dcf82", "1d1003a489054e69825b7a118836e709", "c2c9ff6a81c748448fde3c2096423eac", "e28ed344f7fe4212abae164fa329fc2a", "8ab98ce09f494ad691d6e790c429df70" ] }, "id": "9qlchNqwWn80", "outputId": "ecc263d7-ae42-4a2a-f545-b0d59addbce4" }, "execution_count": 27, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/3760 [00:00