diff --git a/Dockerfile b/Dockerfile index 109dc31ceb32e6e40f10877f9c4a1d8621e087b6..34ccd6f504d95b0a8406efbdb3ae0f6e5aff1c21 100644 --- a/Dockerfile +++ b/Dockerfile @@ -27,6 +27,7 @@ COPY --chown=user . . FROM base as test RUN pip install .[test] +# TODO don't run all at once because of memory errors? RUN python -m pytest tests FROM python:3.10-slim as deploy diff --git a/README.md b/README.md index 327b52f0efef440553fde12329e28290eb57d8d4..c4977903a11f04c5b091f0b77576ea91746280e2 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,7 @@ Note that you might have to `sudo service docker start` first. + The application should then be available at http://localhost:8000. For the API, see the `openapi.yaml` file. Docker-compose mounts a volume and listens to changes in the source code, so the application will be reloaded and @@ -35,7 +36,15 @@ reflect them. We use multi-stage builds to reduce the image size, ensure flexibility in requirements and that tests are run before each deployment. However, while it does reduce the size by nearly 3GB, the resulting image still contains deep learning libraries and -pre-downloaded models, and will take around 7GB of disk space. +pre-downloaded models, and will take around 9GB of disk space. + +NOTE: Since the service is hosting two large deep learning models, there might be memory issues depending on your +machine, where the terminal running +docker would simply crash. +Should that happen, you can try increasing resources allocated to docker, or splitting commands in the docker file, +e.g., running tests one by one. +If everything fails, you can still use the hosted huggingface hub demo, or follow the steps below and run the app +locally without Docker. Alternatively, you can setup a python environment by hand. It is recommended to use a virtualenv. Inside one, run ```bash @@ -43,6 +52,8 @@ pip install -e .[test] ``` the `[test]` option makes sure to install test dependencies. +Then, run `python app.py` or `uvicorn --host 0.0.0.0 --port 8000 "app:app" --reload` to run the application. + If you intend to perform training and evaluation of deep learning models, install also using the `[training]` option. ### Running tests @@ -91,7 +102,7 @@ dataset are as follows: | Model | precision | recall | F1 | support | |----------|-----------|--------|------|---------| | baseline | 0.79 | 0.72 | 0.75 | 10079 | -| ours* | 0.86 | 0.85 | 0.85 | 10079 | +| ours* | 0.84 | 0.84 | 0.84 | 10079 | *details of the fine-tuning process in the next section. We treat each comma as one token instance, as opposed to the original paper, which NER-tags the whole multiple-token diff --git a/commafixer/src/fixer.py b/commafixer/src/fixer.py index 2b050a87cd4fc567542dc3bf13bad5d3180bcf14..0afd1198e0fe69e677fc8ea57e5c5eaf3474fcac 100644 --- a/commafixer/src/fixer.py +++ b/commafixer/src/fixer.py @@ -52,7 +52,8 @@ class CommaFixer: tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) model = PeftModel.from_pretrained(inference_model, model_name) model = model.merge_and_unload() # Join LoRa matrices with the main model for faster inference - return model, tokenizer + # TODO batch, and move to CUDA if available + return model.eval(), tokenizer def _fix_commas_based_on_labels_and_offsets( diff --git a/notebooks/evaluation.ipynb b/notebooks/evaluation.ipynb index d6ba034d7678677b7caf417cd070b6923954af71..9f31d2cb1ef7e815dae53476f82c0376f16b8a5c 100644 --- a/notebooks/evaluation.ipynb +++ b/notebooks/evaluation.ipynb @@ -1,5341 +1,3344 @@ { - 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" 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" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fdc21758404b42fab913402933c0e7cc", + "version_major": 2, + "version_minor": 0 }, - "execution_count": null, - "outputs": [] + "text/plain": [ + "Generating validation split: 0%| | 0/3760 [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": [ { - "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": [] + "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": [ { - "cell_type": "markdown", - "source": [ - "Preparing the dataset with removed punctuation, needed for the baseline model." + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/3760 [00:00 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