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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": null, "outputs": [] }, { "cell_type": "code", "source": [ "assert evaluate([gt], [pred]) == (1.0, 0.4)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SVuOoTWxUvWm", "outputId": "1ffd4413-0573-4611-8ecd-e499ee5a975c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " 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", "\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": null, "outputs": [] }, { "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": null, "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": 159, "referenced_widgets": [ "f40f93b22e1c462d9f6b1efd7cccab5a", "f7178fd2bf9b4f53a5936c908536cb18", "03d6e4db2b374efab79e6b516009c272", "7830bb1ae83d40eea66f9ce2acb30fb0", "f1ef3518025c491382bbcaa91b31d3e9", "8f59bbba2aa44cbc81d4766e340f1c8d", "030b1660a12942daab639a87b22bd6d3", "543920e784c84f1c902af8980dcfa279", "ff699eb9bd904f5ea9fe9c747c48404a", "78c438539cde4fcd83c7ef3bf679fa8b", "9c0a0d71d418406e9e312e3ec2f538a0" ] }, "id": "9qlchNqwWn80", "outputId": "24751d51-e8b8-4ac8-9ba9-020634faf91e" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/3760 [00:00