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"source": [ "If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n", "\n", "To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.\n", "\n", "First you have to store your authentication token from the Hugging Face website (in your account) then execute the following cell and input your username and password:" ], "metadata": { "id": "u4MhZpe_BA6-" } }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359, "referenced_widgets": [ "73361761a76d4889882844e561635d73", "d00d28d5a8da40d2b2daf050d11f35bd", "d50395d307f74618bd1f7bc779de9917", "952b2b041fd641d7a574ecdce087a253", "3c5fb8df94b14e70803d694df1970489", "53ca6eeb14934f8bae3d91005d7c8d67", "46d95a0e50b34b9091850a0d6a3db2de", "77b1c53abd094c50819653688d97f47d", "1b6f7d1e344e44e7baa789c28411e5c9", "cb7a15b0f32a493bacea8b577679bdfb", "21c40f33301c47fbb608102923de85f8", "cffc36cf5d5b4e90b1c84539db353748", "87883364cb514bdf9c28a05a9eb1cb77", "fe85cb9d4e404a74bdd1d297749cb997", "e394a62914984ca3b7af0e6a3112f5b9", "28e488b6026744b6863f1a290549c316", "be8149c67e3d4ff9bf579a432d5ad701" ] }, "id": "U-J05hwfBL08", "outputId": "ec39411a-9448-45fc-f1c3-172c14f91859" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
\", \"\"\n", "]\n", "# if you want to train the tokenizer on both sets\n", "# files = [\"train.txt\", \"test.txt\"]\n", "# training the tokenizer on the training set\n", "files = [\"train.txt\"]\n", "# 30,522 vocab is BERT's default vocab size, feel free to tweak\n", "vocab_size = 30_522\n", "# maximum sequence length, lowering will result to faster training (when increasing batch size)\n", "max_length = 512\n", "# whether to truncate\n", "truncate_longer_samples = False" ], "metadata": { "id": "bOBSHkbFuu5H", "execution": { "iopub.status.busy": "2023-06-16T00:28:15.046095Z", "iopub.execute_input": "2023-06-16T00:28:15.046442Z", "iopub.status.idle": "2023-06-16T00:28:15.052095Z", "shell.execute_reply.started": "2023-06-16T00:28:15.046420Z", "shell.execute_reply": "2023-06-16T00:28:15.050980Z" }, "trusted": true }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import transformers\n", "from transformers import AutoTokenizer\n", "\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n", "assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast)\n", "\n", "\n", "# # initialize the WordPiece tokenizer\n", "# tokenizer = BertWordPieceTokenizer()\n", "# # train the tokenizer\n", "# tokenizer.train(files=files, vocab_size=vocab_size, special_tokens=special_tokens)\n", "# # enable truncation up to the maximum 512 tokens\n", "# tokenizer.enable_truncation(max_length=max_length)" ], "metadata": { "id": "-CVoZ3bC_j6K", "execution": { "iopub.status.busy": "2023-06-16T00:29:11.214924Z", "iopub.execute_input": "2023-06-16T00:29:11.215259Z", "iopub.status.idle": "2023-06-16T00:29:12.766231Z", "shell.execute_reply.started": "2023-06-16T00:29:11.215236Z", "shell.execute_reply": "2023-06-16T00:29:12.764863Z" }, "trusted": true, "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "aa65f73e671f4ca886d5243786cff482", "6f4b59e106bb4559a62cb6ef1e317930", "ec71c84bceab40eca3e274dd5525ff83", "56fd51be14bb432cbe747f5c1a51468d", "e65095d8e9d4478e9f57fa19b2cdf0e8", "baf0e820f5e142f8ab4c8d3deed72608", "c30629a840b3456eaac6a9702b2cedc9", "68a503ffb12448078dc402947faf2403", "199c0f0bb9634539a33329334eb1609c", "004ae3ec389244b6abe1fce26f7d5573", "9339dfdc705c4f7fa6cb259a382d26d2", "5d01a359f54b45a49bc2b69f6b769b61", "4f12daad8f3c4b7da5e190956971ae43", "51a10089effe460a8eb5959a6720898c", "31eeccc5c83a4a6d8b609415065affdd", "5a5fb6046e7a44a3a9b7f76f7a8dd0d8", "621213d4bf2e454caac49d5d9dc70da0", "d866785a13ef4d7a966033f3c5bf0690", "9df336bca8af42909fe4d0e0bf0e4801", "1f773bd126a64ed7a2811285ee649c53", "f085350218b145978f245a512d5b4a0b", "b76b2fb808f449f79c03ab826a061aaf", "6934e0438d6e45039b843dfb8d7fcdf9", "c98119f661fe4bfaaf892dffa017e379", "0210001520d24f96b83f79602cc7219a", "38470542986a4fad891a1a54b4e99433", "cd0768aba1fe48c09209cde3f35f8644", "be34f467900b400ebec1e39b7a98bd16", "10508fc470bd436a87a8d5554b9cccb3", "3006d2dd77f54dc18c0d98ae5332281f", "67170aa447104b5e94f4233863c5a7fb", "847b52f433454cd5911c9f2f1ec1af26", "142e2e4e197c4cf5b22c0a8e033ba464", "65912f982b064948831a3ec46298fc7f", "dd1b40c935ae404ba9cecf01d28ee438", "1da9adffdda74b34b7ec92653e851cd3", "e5ed339ec9c444e3b12e8ea2de3d1a26", "36c80c2f79534088afbb5c46b80f3d18", "3a3edad4cfd649399d149341ba0fd25d", "52699e5a36864f6ea7d88e3240d9b56d", "4aa81a5f34894f6b928c849d6b088d1d", "70830c4ee13c4947825c4977968f8151", "068b13bef47444ffbb5d646f8e2b2ef5", "89a5f0be6c444b84a4b43a8c83151c9f" ] }, "outputId": "36b60e13-3f7d-462c-e3ac-d9bdd037ac35" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)okenizer_config.json: 0%| | 0.00/28.0 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_checkpoint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m args = TrainingArguments(\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\"claims-data-model\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mevaluation_strategy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"epoch\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2e-5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'model_checkpoint' is not defined" ] } ] }, { "cell_type": "markdown", "source": [ "Here we set the evaluation to be done at the end of each epoch, tweak the learning rate, use the `batch_size` defined at the top of the notebook and customize the number of epochs for training, as well as the weight decay.\n", "\n", "The last argument to setup everything so we can push the model to the Hub regularly during training. Remove it if you didn't follow the installation steps at the top of the notebook. If you want to save your model locally in a name that is different than the name of the repository it will be pushed, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `\"sgugger/bert-finetuned-ner\"` or `\"huggingface/bert-finetuned-ner\"`).\n", "\n", "Then we will need a data collator that will batch our processed examples together while applying padding to make them all the same size (each pad will be padded to the length of its longest example). There is a data collator for this task in the Transformers library, that not only pads the inputs, but also the labels:" ], "metadata": { "id": "BJpVmVpzo6iC" } }, { "cell_type": "code", "source": [ "from transformers import DataCollatorForTokenClassification\n", "\n", "data_collator = DataCollatorForTokenClassification(tokenizer)" ], "metadata": { "id": "kmFCTByJ1OI3", "trusted": true }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "The last thing to define for our Trainer is how to compute the metrics from the predictions. Here we will load the `seqeval` metric (which is commonly used to evaluate results on the CONLL dataset) via the Datasets library." ], "metadata": { "id": "0ZLL5p95o6iC" } }, { "cell_type": "code", "source": [ "metric = load_metric(\"seqeval\")" ], "metadata": { "id": "7Bie2oq0o6iC" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "This metric takes list of labels for the predictions and references:" ], "metadata": { "id": "XdqxwJEOo6iC" } }, { "cell_type": "code", "source": [ "labels = [label_list[i] for i in example[\"tags\"]]\n", "metric.compute(predictions=[labels], references=[labels])" ], "metadata": { "id": "dVdwnxe8o6iD" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "So we will need to do a bit of post-processing on our predictions:\n", "\n", "- select the predicted index (with the maximum logit) for each token\n", "- convert it to its string label\n", "- ignore everywhere we set a label of -100\n", "\n", "The following function does all this post-processing on the result of Trainer.evaluate (which is a namedtuple containing predictions and labels) before applying the metric:" ], "metadata": { "id": "gSE2DwIUqvHe" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "\n", "def compute_metrics(p):\n", " predictions, labels = p\n", " predictions = np.argmax(predictions, axis=2)\n", "\n", " # Remove ignored index (special tokens)\n", " true_predictions = [\n", " [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", " ]\n", "\n", " true_labels = [\n", " [label_list[l] for (p, l) in zip (prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", " ]\n", "\n", " results = metric.compute(predictions=true_predictions, references=true_labels)\n", " return {\n", " \"precision\": results[\"overall_precision\"],\n", " \"recall\": results[\"overall_recall\"],\n", " \"f1\": results[\"overall_f1\"],\n", " \"accuracy\": results[\"overall_accuracy\"],\n", " }" ], "metadata": { "id": "OMKVmXZN2o7c", "trusted": true }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Note that we drop the precision/recall/f1 computed for each category and only focus on the overall precision/recall/f1/accuracy.\n", "\n", "Then we just need to pass all of this along with our datasets to the `Trainer`:" ], "metadata": { "id": "3YlGOBOqsKNK" } }, { "cell_type": "code", "source": [ "trainer = Trainer(\n", " model,\n", " args,\n", " train_dataset=tokenized_datasets[\"train\"],\n", " eval_dataset=tokenized_datasets[\"test\"],\n", " data_collator=data_collator,\n", " tokenizer=tokenizer,\n", " compute_metrics=compute_metrics\n", ")" ], "metadata": { "id": "HYsgN58E2tFD", "trusted": true }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "We can now finetune our model by just calling the `train` method:" ], "metadata": { "id": "rxgoaktmtw7m" } }, { "cell_type": "code", "source": [ "trainer.train()" ], "metadata": { "id": "vJO-1w15ARHs", "trusted": true }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "The `evaluate` method allows you to evaluate again on the evaluation dataset or on another dataset:" ], "metadata": { "id": "b8apIxuNz5Li" } }, { "cell_type": "code", "source": [ "trainer.evaluate()" ], "metadata": { "id": "8ROoCqpssCb9", "trusted": true }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "To get the precision/recall/f1 computed for each category now that we have finished training, we can apply the same function as before on the result of the `predict` method:" ], "metadata": { "id": "GU_4nek00M9z" } }, { "cell_type": "code", "source": [ "predictions, labels, _ = trainer.predict(tokenized_datasets[\"test\"])\n", "predictions = np.argmax(predictions, axis=2)\n", "\n", "\n", "# Remove ignored index (special tokens)\n", "true_predictions = [\n", " [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", "]\n", "\n", "true_labels = [\n", " [label_list[l] for (p, l) in zip (prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", "]\n", "\n", "results = metric.compute(predictions=true_predictions, references=true_labels)\n", "print(results)" ], "metadata": { "id": "WvVnsyQY0TeI" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Save the model locally if wanted" ], "metadata": { "id": "PRSEZz_k2ql9" } }, { "cell_type": "code", "source": [ "# pt_save_directory = \"./pt_save_pretrained\"\n", "# tokenizer.save_pretrained(pt_save_directory)\n", "# model.save_pretrained(pt_save_directory)" ], "metadata": { "id": "TQvM693K2scM" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Or upload the result of the training to the hub (requires the login setup from near the start)" ], "metadata": { "id": "fLdNGVfKF4ia" } }, { "cell_type": "code", "source": [ "trainer.push_to_hub(\"claims-data-model-2\")\n", "tokenizer.push_to_hub(\"claims-data-model-2\")" ], "metadata": { "id": "hBLFQ8NlF-kM" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `\"your-username/the-name-you-picked\"` so for instance:\n", "\n", "```python\n", "from transformers import AutoModelForTokenClassification\n", "\n", "model = AutoModelForTokenClassification.from_pretrained(\"sgugger/my-awesome-model\")\n", "```" ], "metadata": { "id": "FLr5pdlzGLrv" } }, { "cell_type": "code", "source": [], "metadata": { "id": "cbnDLuVmGUp9" }, "execution_count": null, "outputs": [] } ] }