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Add documentation for SciBERT example

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Andras Schmelczer 2022-07-17 17:13:35 +02:00
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docs/examples/scibert/train.ipynb
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@ -1,210 +1,90 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fine-tune SciBERT\n",
"\n",
"We are planning to do a simple classification task on scientific text. For that, [SciBERT](https://github.com/allenai/scibert) is an ideal model to fine-tune since it has been pretrained of academic publications.\n",
"\n",
"This notebook was updated so that it can run in [Google Colab](https://colab.research.google.com/).\n",
"\n",
"First, we need to install the dependencies."
]
},
{
"cell_type": "code",
"execution_count": 48,
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 2529,
"status": "ok",
"timestamp": 1656596749103,
"user": {
"displayName": "Schmelczer András",
"userId": "08401926777942666437"
},
"user_tz": -120
},
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"outputId": "88a9931b-396a-4cf1-c659-8a7b098b3cdd"
},
"outputs": [],
"source": [
"!pip install transformers datasets great-ai > /dev/null"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the training data from S3. (We have uploaded this to S3 in the `data` notebook.)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"name": "stderr",
"output_type": "stream",
"text": [
"evaluation-experiment-2-stage #1-2m6dmb.json\n",
"evaluation-experiment-2-stage #1-sa6a0y.json\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.20.1)\n",
"Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (2.3.2)\n",
"\u001b[31mERROR: Could not find a version that satisfies the requirement great-ai==0.0.6 (from versions: none)\u001b[0m\n",
"\u001b[31mERROR: No matching distribution found for great-ai==0.0.6\u001b[0m\n"
"\u001b[38;5;39mLatest version of summary-train-dataset-small is 0 (from versions: 0)\u001b[0m\n",
"\u001b[38;5;39mFile summary-train-dataset-small-0 found in cache\u001b[0m\n"
]
}
],
"source": [
"from pathlib import Path\n",
"from great_ai.large_file import LargeFileS3\n",
"import json\n",
"\n",
"annotations = []\n",
"for p in Path(\".\").glob(\"*.json\"):\n",
" with open(p, encoding=\"utf-8\") as f:\n",
" print(p)\n",
" annotations.append(json.load(f))\n",
"LargeFileS3.configure_credentials_from_file(\"config.ini\")\n",
"\n",
"evaluations = {\n",
" sentence: [\n",
" annotation[sentence] for annotation in annotations if sentence in annotation\n",
" ]\n",
" for sentence in {\n",
" sentence for annotation in annotations for sentence in annotation.keys()\n",
" }\n",
"}\n",
"\n",
"X = [s for s in evaluations.keys()]\n",
"y = [int(sum(e) > 0) for e in evaluations.values()]\n",
"\n",
"# !pip install transformers datasets great-ai==0.0.6"
"with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f:\n",
" # splitting training and test data is done later by `datasets`\n",
" X, y = json.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finetune SciBERT, for more info about this step, check out [HuggingFace](https://huggingface.co/docs/transformers/training).\n",
"If you're only here for `great-ai`, feel free to skip the next cell."
]
},
{
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"metadata": {
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
"loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"pad_token_id\": 0,\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.20.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 31090\n",
"}\n",
"\n",
"loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/33593020f507d72099bd84ea6cd2296feb424fecd62d4a8edcc2a02899af6e29.38339d84e6e392addd730fd85fae32652c4cc7c5423633d6fa73e5f7937bbc38\n",
"loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer.json from cache at None\n",
"loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/added_tokens.json from cache at None\n",
"loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/special_tokens_map.json from cache at None\n",
"loading file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/tokenizer_config.json from cache at None\n",
"loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"pad_token_id\": 0,\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.20.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 31090\n",
"}\n",
"\n",
"loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"pad_token_id\": 0,\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.20.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 31090\n",
"}\n",
"\n",
"loading configuration file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/858852fd2471ce39075378592ddc87f5a6551e64c6825d1b92c8dab9318e0fc3.03ff9e9f998b9a9d40647a2148a202e3fb3d568dc0f170dda9dda194bab4d5dd\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"allenai/scibert_scivocab_uncased\",\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"pad_token_id\": 0,\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.20.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 31090\n",
"}\n",
"\n",
"loading weights file https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/de14937a851e8180a2bc5660c0041d385f8a0c62b1b2ccafa46df31043a2390c.74830bb01a0ffcdeaed8be9916312726d0c4cd364ac6fc15b375f789eaff4cbb\n",
"Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
"- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at allenai/scibert_scivocab_uncased and are newly initialized: ['classifier.bias', 'classifier.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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@ -219,24 +99,6 @@
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
"PyTorch: setting up devices\n",
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
"/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" FutureWarning,\n",
"***** Running training *****\n",
" Num examples = 400\n",
" Num Epochs = 50\n",
" Instantaneous batch size per device = 32\n",
" Total train batch size (w. parallel, distributed & accumulation) = 32\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 650\n"
]
},
{
"data": {
"text/html": [
@ -330,61 +192,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 100\n",
" Batch size = 32\n",
"Saving model checkpoint to models/checkpoint-13\n",
"Configuration saved in models/checkpoint-13/config.json\n",
"Model weights saved in models/checkpoint-13/pytorch_model.bin\n",
"Deleting older checkpoint [models/checkpoint-91] due to args.save_total_limit\n",
"***** Running Evaluation *****\n",
" Num examples = 100\n",
" Batch size = 32\n",
"Saving model checkpoint to models/checkpoint-26\n",
"Configuration saved in models/checkpoint-26/config.json\n",
"Model weights saved in models/checkpoint-26/pytorch_model.bin\n",
"Deleting older checkpoint [models/checkpoint-117] due to args.save_total_limit\n",
"***** Running Evaluation *****\n",
" Num examples = 100\n",
" Batch size = 32\n",
"Saving model checkpoint to models/checkpoint-39\n",
"Configuration saved in models/checkpoint-39/config.json\n",
"Model weights saved in models/checkpoint-39/pytorch_model.bin\n",
"Deleting older checkpoint [models/checkpoint-130] due to args.save_total_limit\n",
"***** Running Evaluation *****\n",
" Num examples = 100\n",
" Batch size = 32\n",
"Saving model checkpoint to models/checkpoint-52\n",
"Configuration saved in models/checkpoint-52/config.json\n",
"Model weights saved in models/checkpoint-52/pytorch_model.bin\n",
"Deleting older checkpoint [models/checkpoint-143] due to args.save_total_limit\n",
"***** Running Evaluation *****\n",
" Num examples = 100\n",
" Batch size = 32\n",
"Saving model checkpoint to models/checkpoint-65\n",
"Configuration saved in models/checkpoint-65/config.json\n",
"Model weights saved in models/checkpoint-65/pytorch_model.bin\n",
"Deleting older checkpoint [models/checkpoint-156] due to args.save_total_limit\n",
"***** Running Evaluation *****\n",
" Num examples = 100\n",
" Batch size = 32\n",
"Saving model checkpoint to models/checkpoint-78\n",
"Configuration saved in models/checkpoint-78/config.json\n",
"Model weights saved in models/checkpoint-78/pytorch_model.bin\n",
"Deleting older checkpoint [models/checkpoint-13] due to args.save_total_limit\n",
"***** Running Evaluation *****\n",
" Num examples = 100\n",
" Batch size = 32\n",
"Saving model checkpoint to models/checkpoint-91\n",
"Configuration saved in models/checkpoint-91/config.json\n",
"Model weights saved in models/checkpoint-91/pytorch_model.bin\n",
"Deleting older checkpoint [models/checkpoint-26] due to args.save_total_limit\n",
"***** Running Evaluation *****\n",
" Num examples = 100\n",
" Batch size = 32\n",
"Saving model checkpoint to models/checkpoint-104\n",
"Configuration saved in models/checkpoint-104/config.json\n",
"Model weights saved in models/checkpoint-104/pytorch_model.bin\n",
"...\n",
"Deleting older checkpoint [models/checkpoint-39] due to args.save_total_limit\n",
"***** Running Evaluation *****\n",
" Num examples = 100\n",
@ -418,6 +226,7 @@
" TrainingArguments,\n",
" EarlyStoppingCallback,\n",
")\n",
"from pathlib import Path\n",
"import numpy as np\n",
"from datasets import Dataset, load_metric\n",
"\n",
@ -474,13 +283,17 @@
").train()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The best macro F1-score on the test set is **0.89** which is (not surprisingly) substantially more than the SVM achieved. We have a great model, it's time to deploy it. But first, we have to store it in a secure place."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 25368,
"status": "ok",
@ -514,387 +327,46 @@
}
],
"source": [
"from great_ai.large_file import LargeFileS3\n",
"\n",
"LargeFileS3.configure_credentials_from_file(\"config.ini\")\n",
"from great_ai import save_model\n",
"\n",
"# save Torch model to local disk\n",
"model.save_pretrained(\"pretrained\")\n",
"LargeFileS3(\"scibert-highlights\").push(\"pretrained\")"
"\n",
"# upload model from local disk to S3\n",
"# (because the S3 credentials have been already set, `save_model` will use LargeFileS3)\n",
"save_model(\"pretrained\", key=\"scibert-highlights\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next: [Part 3](/examples/scibert/deploy)"
]
}
],
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"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "Copy of Forms snippets",
"provenance": [
{
"file_id": "/v2/external/notebooks/snippets/forms.ipynb",
"timestamp": 1656585404621
}
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"gpuClass": "standard",
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"display_name": "Python 3.10.4 ('.env': venv)",
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