{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "JeFpEz-XyZTx" }, "source": [ "##### This work was done by me as a part my academic project\n", "##### *problem description and dataset used,* [*clic*](https://huggingface.co/datasets/owaiskha9654/PubMed_MultiLabel_Text_Classification_Dataset_MeSH)\n", "[*link to github repo &colab*](https://github.com/hajar-hajji/2A-project-MinesNancy-Loria/blob/main/MedicalAbstracts_MultilabelClassification/MedicalAbstracts_multilabel_classification.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "id": "OD5qRiZBpmEd" }, "source": [ "#### Loading our dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ylzZnhXGpf7E", "outputId": "0d5848d0-c163-4cd1-e260-93cad5136164" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting 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] } ], "source": [ "!pip install datasets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TG9KdRLApVCH" }, "outputs": [], "source": [ "from datasets import load_dataset\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 233, "referenced_widgets": [ "7bb7b488912c41229c4f52c10acf418d", "a491db0de6754701bec1ed0b1dee0b53", "4db9a812ea484b5aae744d7f99daf213", "8a7ea158d2324f5b922f83d9446fb11c", "66482f353af940cca612735c6f148a71", "b8ee6a5794b449e3bb949820c3101795", "71404edcc0544537a68d4e375834a3fe", "66269ad51da14492bf589f9e8ed3212a", "2fd2f67e9bec4bbdb846b9168f64243c", "ab40298ef8b64f9c81a4f59cf777a8a3", "767d4718e0584a859937644ba1e6de65", "29a4f5bb2f0f40a8862b03c1b592dc6e", "e07cd85e9b3f4c77907804960cb1b7f7", "12c3e050593c4cc5a519d613fa99c6c3", "6357b53dd0e5494cbae0915bbb09d70d", "085daa87d602495ba387505d7addcfd7", "35630fa379304dbc9fafce769b68be46", "e015adba4713426584761ca3617c6932", "d347390f96784e75945cc51d68f39cec", "6743d48d260f44648cc44f4b807900c3", "949fc2a0e36542069885c352a52ddf2a", "8c81c573a7ad4cad824281e9db0ae532", "62746b245cc6426ca40e64b207f18764", "717cb8d537464ad59192cae89d587cef", "51f15e08de964d6588fee19136177005", "48650c3259c646878aa245b564e74237", "593e3dd323be4dd6881f0ebf16734c26", "6dadd7a4140d46b6a101274a8560b1f3", "b8960ed31e1f4b33b4bd37c144e19e7e", "9f1ad8b2589b4810883cab1b8f3ff403", "c118d1bf0f7b46ddbaf2edbe615357a3", "ac62656c960d432f94c1ada2a1e6c3c8", "b1aef87d7b104c458b3d52367e38c64b", "79b5773782504dfca6d9cdbefbfe4c4b", "323a7fff25e140ffa0a0797759e235f0", "602718a0005d48bc8976a96e1220b91b", "f4431575c50740828c194978237f83e6", "55f38c1e1e1d42ba8dbc167dc25eacd1", "548d6d1baf8d44cca7983b1e2fa30145", "27bd13b7007443c0b80e240094c3548e", "46507d61346844eeba74910e3fd17d30", "e1357c899dfc4cc7b243675a5b28c0b9", "a256244cf3f740fdbc9618136f426ed6", "bea9ce0783b34ddd9fdb691342f65679", "472d4c6210034dd8b2835f27894aee99", "43136e63ca7348d1994eeb8fa86a16c2", "eb9c53ff17334912be09fdd689fe2ba8", "8d79552fcf554659822238a18c496be4", "48a7dfd985684a6a86a3225ba58292eb", "2a5e3f0bfd8c4ef5a1f8c400e249b806", "87f6ca0755044889a48ecc4901ece749", "afff27a60ead4540aeb63abca824a1c2", "5c149ee82a1548da800df4ffe8412195", "37d80c0a5bc14d9097112a5ff1c11b96", "50d2844a0b124e8ea86b487d0e735f09", "2949b640914841468038d426ba386dc8", "91f98c2cbb66427c827450fe16a3b581", "088ac21fdf7e4861b5ffb816c5f1f0cb", "56e78a2746b440058bc13f4ef4e4e04e", "62ede59767c94e27bbc50e759a6aad61", "de93611cf1074616be5b92133210c630", "92d7ffaf03c844028e5ec5222a586ab2", "b1c53c27bfbd4ff18b676c43f58cde5b", "9f800503050a4537ba2e2cac44662bbc", "1302ca8fba494e8089548b5aa4e7c4bd", "3e9041e4729d4c8d9cd1abcbf99fa215" ] }, "id": "rIKtQ7c3wfxI", "outputId": "2cf8f3e0-de81-492c-efe4-a3ecd0810264" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7bb7b488912c41229c4f52c10acf418d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/960 [00:00=0.11.1\n", " Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m107.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.12.1)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (23.0)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) 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"471b020ec53944b6827fe487657e135c", "2d91a540467846a181dc1477044f6cf4", "f0e742d9b2db40e8b57e084de9420c40", "f6f9fb55c39a46529329d1620e7d6088", "f754f438d35e421db9ccf52d0c228f0a", "7abadd0359364223833b5809140c99db", "a46f3a4d96f24d0daf6dab9e63cfa8a9", "a477991f543949c7bb24569d6cbbe8d8", "d51d0c1236334849b5fc6c3e547360f4", "16aba8f9a11a459f9cf1059efdc12ec9", "c6baaa24df4943f6a7b8be154ef9e18a", "27302aee438f4637be2daa2c74c478c7", "23812f0e09cf4e57b91df171f66d1f54", "a5c81d14bd204b9fab508b7ae315b5d9", "eb33df63f64e42a3933400de0d955bc3", "d2e2ae1d9fd34ff6ae20235d20cac2d7", "c41cad9f1efc4bbc80d8c3c4e6c776f8" ] }, "id": "QA-rqvoVqI1v", "outputId": "35dbd8aa-e8c6-49a2-e052-aef621fcbb53" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "564a62ba9281479e992567a376ee17ce", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/232k [00:00max_length:\n", "# max_length=len(mydataset[\"train\"][\"fullText\"][i])\n", "# max_sentence=sentence\n", "# print(max_sentence)\n", "# print(max_length)\n", "\n", "# \"\"\"ps. do not rerun this cell !!!!!! it takes so long to run and it's of no use for the study as i already chose max_len ;)\n", "# \"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "f63Tldlq6UWF" }, "outputs": [], "source": [ "###now let's define a function that will apply these steps for a certain dataset\n", "def preprocessing(sample):\n", " #####Encode all texts###############\n", " #i'll set max_length to 512\n", " mytext=sample[\"fullText\"]\n", " encoder=tokenizer(mytext,\n", " is_split_into_words=False, #phrases non tokenisées\n", " padding=\"max_length\", truncation=True, #assure all inputs have same len\n", " max_length=512)\n", " #returns dictionary : {\"input_ids\"}\n", " \n", " #####Encode all labels###############\n", " #create dictionary that contains labels \n", " dic_labels={key:sample[key] for key in sample.keys() if key in tags}\n", " #create an empty matrix\n", " columns=len(tags)\n", " lines=len(sample[\"A\"])\n", " mymatrix=np.zeros((lines,columns))\n", "\n", "\n", " for i,k in enumerate(dic_labels.keys()):\n", " mymatrix[:,i]=dic_labels[k]\n", " #that way we'll have a matrix where each line of labels corresponds to a specific text, 1 if label and 0 else...\n", "\n", " #add labels to the main dictionary\n", " encoder[\"labels\"]=mymatrix.tolist()\n", " #delete token_type_ids bcs we won't need it\n", " del encoder[\"token_type_ids\"] # if AutoModelForSequenceClassification model is used, it will ignore the token_type_ids argument anyway.\n", "\n", " return encoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "3ddc743a17cc42dfaddbd590417fd4f1", "db68936431e842068b7378c75bff17f7", "b8f8d75e04ba4ac49f48b868a93e56e3", "0a5649e245804023953ad2114e65f933", "82b865bb3b674a549e3b540c88ca7fd7", "d2cfe948df1e4a56b61f50dd1823f682", "9ee44e3b1e97457b812068f8ea6195aa", "ddcd7fb8ec2f4b09871542c457983c59", "2bb55e38536140888c3599621f39b928", "35abfcc74487460695b92748599a6304", "4cdc3407a49b42d1b776c6e8f943e543" ] }, "id": "XkZyfXkwE7Zh", "outputId": "c612d02e-3529-49da-f296-b12137d0babc" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3ddc743a17cc42dfaddbd590417fd4f1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/50000 [00:00\n", "\n" ] } ], "source": [ "from datasets import Dataset\n", "import torch\n", "\n", "#transform to dataset object\n", "train_data_copy=Dataset.from_dict(train_data)\n", "val_data_copy=Dataset.from_dict(val_data)\n", "print(type(train_data_copy))\n", "\n", "# import copy\n", "# train_data_copy2=copy.deepcopy(train_data_copy)\n", "\n", "###transform all datasets to tensors before feeding them to the model(ps: directly w/ affecting variables...!!)\n", "train_data_copy.set_format(\"torch\")\n", "print(type(train_data_copy))\n", "val_data_copy.set_format(\"torch\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9jHpa155kfZr" }, "outputs": [], "source": [ "# from datasets import DatasetDict\n", "\n", "# train_data_copyy=DatasetDict(train_data)\n", "# train_data_essai=train_data.copy()\n", "# train_data_copy.set_format(\"torch\")\n", "##\"\"--\n", "# input_ids_train=train_data[\"input_ids\"]\n", "# mask_train=train_data[\"attention_mask\"]\n", "# labels_train=train_data[\"labels\"]\n", "# tensor_train=TensorDataset(torch.tensor(input_ids_train),torch.tensor(mask_train),torch.tensor(labels_train))" ] }, { "cell_type": "markdown", "metadata": { "id": "9lqu68T1rzCf" }, "source": [ "#### Introducing the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 159, "referenced_widgets": [ "f50a069bf0b445a38f809c7872fc2153", "d68e445c62ef4472a10212f8122fd10e", "f15de53617b54f61a73069be93a2a56e", "5ea87c870f124556a2da90e1dcd80d4d", "a465b55b480444ed8cdb2d70ab50466d", "49702ffa478b46f0a318fca7dac1f28c", "e329dea9634647b2aa840ddbb93973bf", "d67dcac374c74096b6811d04641f13dd", "da0ffcc86fa1438aa0b4151a89bdcd0c", "d434c7b3bd914bf79df5b8ee18c2045d", "017e29c8f4ee42b0a6f54bf091c63f76" ] }, "id": "xhWKzObAouwZ", "outputId": "e1c6d2dd-0e44-4f02-b95f-bc32fd18a840" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f50a069bf0b445a38f809c7872fc2153", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/440M [00:00), logits=tensor([[-0.0321, 0.2688, 0.1807, -0.3697, 0.3948, -0.1747, -0.4591, -0.1441,\n", " 0.2316, 0.4735, -0.1143, 0.5607, -0.8226, -0.3508]],\n", " grad_fn=), hidden_states=None, attentions=None)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#let's see what the model output looks like (initially)\n", "output_1 = model(input_ids=train_data_copy[\"input_ids\"][0].unsqueeze(0),labels=train_data_copy[\"labels\"][0].unsqueeze(0))\n", "output_1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RCOYyZ4k8_LI", "outputId": "cee941c6-95ce-42ab-fcb8-7cf85ba8016f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.7532, grad_fn=)\n", "\n", "tensor([[-0.0321, 0.2688, 0.1807, -0.3697, 0.3948, -0.1747, -0.4591, -0.1441,\n", " 0.2316, 0.4735, -0.1143, 0.5607, -0.8226, -0.3508]],\n", " grad_fn=)\n", "\n", "torch.Size([1, 14])\n" ] } ], "source": [ "print(output_1[0]) #loss\n", "print()\n", "print(output_1[1]) #logits\n", "#ps : not normalized, meaning that they're not necessarily between 0 and 1 and are not interpretable as probabilities...\n", "print()\n", "print(output_1[1].shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PmqsMaDq_a1T", "outputId": "f9d7cde3-920d-4674-cdef-9ed552dab307" }, "outputs": [ { "data": { "text/plain": [ "tensor([[0.4920, 0.5668, 0.5451, 0.4086, 0.5974, 0.4564, 0.3872, 0.4640, 0.5576,\n", " 0.6162, 0.4714, 0.6366, 0.3052, 0.4132]], grad_fn=)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "probabilities=torch.sigmoid(output_1[1])\n", "probabilities" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ccFat7uy_75B", "outputId": "b7cfd76a-3d8e-4df6-d05d-182dd7f34ca9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial predictions :\n", "[0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0]\n", "['B', 'C', 'E', 'I', 'J', 'M']\n", "['Organisms', 'Diseases', 'Analytical, Diagnostic and Therapeutic Techniques, and Equipment', 'Anthropology, Education, Sociology, and Social Phenomena', 'Technology, Industry, and Agriculture', 'Named Groups']\n", "\n", "\n", "True labels :\n", "tensor([1., 1., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0., 1., 0.])\n", "['A', 'B', 'E', 'G', 'L', 'N']\n", "['Anatomy', 'Organisms', 'Analytical, Diagnostic and Therapeutic Techniques, and Equipment', 'Phenomena and Processes', 'Information Science', 'Health Care']\n" ] } ], "source": [ "print(\"Initial predictions :\")\n", "predictions=[1 if el>0.5 else 0 for el in probabilities.tolist()[0]]\n", "print(predictions)\n", "literal_predictions=[get_tag[i] for i,el in enumerate(predictions) if el==1]\n", "print(literal_predictions)\n", "interpreted_pred=[main_ref[tag] for tag in literal_predictions]\n", "print(interpreted_pred)\n", "print(\"\\n\")\n", "print(\"True labels :\")\n", "#real labels\n", "labels=train_data_copy[\"labels\"][0]\n", "print(labels)\n", "lit=[get_tag[i] for i,el in enumerate(labels) if el==1]\n", "print(lit)\n", "ref=[main_ref[tag] for tag in lit]\n", "print(ref)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KokOLQdE6KwG", "outputId": "9600787e-c9e8-4c39-a337-2f56bf89e4c0" }, "outputs": [ { "data": { "text/plain": [ "SequenceClassifierOutput(loss=tensor(0.7798, grad_fn=), logits=tensor([[-0.0321, 0.2688, 0.1807, -0.3697, 0.3948, -0.1747, -0.4591, -0.1441,\n", " 0.2316, 0.4735, -0.1143, 0.5607, -0.8226, -0.3508],\n", " [-0.0610, 0.2731, 0.1877, -0.3594, 0.3711, -0.2088, -0.4294, -0.1975,\n", " 0.2379, 0.4534, -0.0987, 0.5458, -0.7977, -0.2637]],\n", " grad_fn=), hidden_states=None, attentions=None)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "output_2 = model(input_ids=train_data_copy[\"input_ids\"][:2],labels=train_data_copy[\"labels\"][:2])\n", "output_2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wM4KgjLv9Oe1", "outputId": "5a3e43da-5d27-405c-8fca-efe8f5acbade" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 14])\n" ] } ], "source": [ "print(output_2[1].shape) #dim: (batch_size,num_tags)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TYeyNEqvsgkN" }, "outputs": [], "source": [ "from sklearn.metrics import precision_recall_fscore_support\n", "from transformers import EvalPrediction\n", "\n", "\"\"\"\n", "EvalPrediction class from the Hugging Face Transformers library\n", "The EvalPrediction class has two main attributes: predictions and label_ids. \n", "\n", "1) predictions is a tensor of size (batch_size, num_labels) containing the model's output scores for each example in the batch.\n", "2) label_ids is a tensor of the same size containing the actual labels for each example in the batch.\n", "3) ...\n", "\n", "source : https://huggingface.co/docs/transformers/internal/trainer_utils\n", "\"\"\"\n", "\n", "def function_metrics(p: EvalPrediction): #an instance of EvalPrediction\n", "\n", " logits=p.predictions\n", " probabilities=torch.sigmoid(torch.tensor(logits)) #turn into probabilities using sigmoid activation function\n", " predicted_labels=np.where(probabilities>=0.5,1,0) #set 1 if belongs to class 0 otherwise\n", "\n", " true_labels=p.label_ids\n", " \n", " #precision, recall, f1, _ = precision_recall_fscore_support(true_labels, predicted_labels, average=None)\n", " precision_micro, recall_micro, f1_micro, _ = precision_recall_fscore_support(true_labels, predicted_labels, average='micro')\n", " \n", " return {\n", " 'precision_micro': precision_micro,\n", " 'recall_micro': recall_micro,\n", " 'f1_micro': f1_micro,\n", " }\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DColzDI9sch1" }, "outputs": [], "source": [ "#fast trainer -> https://huggingface.co/docs/transformers/main_classes/trainer\n", "trainer = Trainer(\n", " model,\n", " arguments,\n", " train_dataset=train_data_copy,\n", " eval_dataset=val_data_copy,\n", " tokenizer=tokenizer,\n", " compute_metrics=function_metrics\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KFcyLc3cq5MA" }, "outputs": [], "source": [ "import gc\n", "\n", "gc.collect()\n", "\n", "torch.cuda.empty_cache()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vJ1Sg9rpmwxG" }, "outputs": [], "source": [ "import os\n", "os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_cached_bytes=2147483648,max_split_size_mb=1024'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "xqMmF6gt4QYa", "outputId": "109a86a7-413c-44d3-a3c4-198963c24dd7" }, "outputs": [ { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: 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", " warnings.warn(\n", "***** Running training *****\n", " Num examples = 40000\n", " Num Epochs = 1\n", " Instantaneous batch size per device = 16\n", " Total train batch size (w. parallel, distributed & accumulation) = 16\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 2500\n", " Number of trainable parameters = 109493006\n" ] }, { "data": { "text/html": [ "\n", "
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EpochTraining LossValidation Loss

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EpochTraining LossValidation LossPrecision MicroRecall MicroF1 Micro
10.3085000.3043850.8565040.8337670.844983

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "***** Running Evaluation *****\n", " Num examples = 10000\n", " Batch size = 16\n", "Saving model checkpoint to /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500\n", "Configuration saved in /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500/config.json\n", "Model weights saved in /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500/special_tokens_map.json\n" ] }, { "ename": "KeyError", "evalue": "ignored", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1541\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1542\u001b[0m )\n\u001b[0;32m-> 1543\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 1544\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1545\u001b[0m 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EpochTraining LossValidation LossPrecision MicroRecall MicroF1 Micro
10.3085000.3043850.8565040.8337670.844983
10.3085000.3043850.8565040.8337670.844983

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'eval_loss': 0.3043845593929291,\n", " 'eval_precision_micro': 0.8565042547843883,\n", " 'eval_recall_micro': 0.8337673231855438,\n", " 'eval_f1_micro': 0.8449828644297442}" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.evaluate()" ] }, { "cell_type": "markdown", "metadata": { "id": "3rjh-5c2WWPb" }, "source": [ "####Save the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UAvHpyMGWYWi", "outputId": "1db3dc9a-6e1b-4641-e408-b654b28aa8ea" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Configuration saved in /content/mymodels/Medical_classif_abstracts_1e/config.json\n", "Model weights saved in /content/mymodels/Medical_classif_abstracts_1e/pytorch_model.bin\n" ] } ], "source": [ "#save tokenizer\n", "tokenizer.save_vocabulary(\"/content/mymodels/Medical_classif_abstracts_1e\")\n", "#save trained model\n", "model.save_pretrained(\"/content/mymodels/Medical_classif_abstracts_1e\")" ] }, { "cell_type": "markdown", "metadata": { "id": "UhFTt29UabId" }, "source": [ "####Continue training on +1 epoch" ] }, { "cell_type": "markdown", "metadata": { "id": "BRPjU96CfLs1" }, "source": [ "#####Reloading saved model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Z9zmqN2XXMQH", "outputId": "29375be1-441c-4e11-f260-8b51b1c05495" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "o3OlggVlagEL" }, "outputs": [], "source": [ "model_path=\"/content/drive/MyDrive/mymodels/medical_multiL_abstracts_1e\"\n", "\n", "NEWtokenizer=BertTokenizer.from_pretrained(model_path)\n", "NEWmodel=BertForSequenceClassification.from_pretrained(model_path)" ] }, { "cell_type": "markdown", "metadata": { "id": "Lc9SLxE-fXJg" }, "source": [ "#####Continue training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CeojUUFKfTGe" }, "outputs": [], "source": [ "trainer = Trainer(\n", " NEWmodel,\n", " arguments,\n", " train_dataset=train_data_copy,\n", " eval_dataset=val_data_copy,\n", " tokenizer=NEWtokenizer,\n", " compute_metrics=function_metrics\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 877 }, "id": "8DFfqJyzgV-Y", "outputId": "57198a4c-cc6a-4f94-b44b-3e13430dcfdf" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: 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", " warnings.warn(\n", "***** Running training *****\n", " Num examples = 40000\n", " Num Epochs = 1\n", " Instantaneous batch size per device = 16\n", " Total train batch size (w. parallel, distributed & accumulation) = 16\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 2500\n", " Number of trainable parameters = 109493006\n" ] }, { "data": { "text/html": [ "\n", "

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EpochTraining LossValidation LossPrecision MicroRecall MicroF1 Micro
10.2808000.2793470.8675470.8457910.856531

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "***** Running Evaluation *****\n", " Num examples = 10000\n", " Batch size = 16\n", "Saving model checkpoint to /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500\n", "Configuration saved in /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500/config.json\n", "Model weights saved in /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/mymodels/med_classif_abstract/checkpoint-2500/special_tokens_map.json\n" ] }, { "ename": "KeyError", "evalue": "ignored", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 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EpochTraining LossValidation LossPrecision MicroRecall MicroF1 Micro
10.2808000.2793470.8675470.8457910.856531
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'eval_loss': 0.27934730052948,\n", " 'eval_precision_micro': 0.8675474133295092,\n", " 'eval_recall_micro': 0.8457908809702731,\n", " 'eval_f1_micro': 0.856531011353279}" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.evaluate()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JETO0LwgpmdH", "outputId": "d275ee9d-6f6d-4cf2-cca9-18b41a2fd62b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Configuration saved in /content/drive/MyDrive/mymodels/medical_multiL_abstracts_2e/config.json\n", "Model weights saved in /content/drive/MyDrive/mymodels/medical_multiL_abstracts_2e/pytorch_model.bin\n" ] } ], "source": [ "NEWtokenizer.save_vocabulary(\"/content/drive/MyDrive/mymodels/medical_multiL_abstracts_2e\")\n", "NEWmodel.save_pretrained(\"/content/drive/MyDrive/mymodels/medical_multiL_abstracts_2e\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Y9qartlEpwII" }, "source": [ "####Test on new abstracts" ] }, { "cell_type": "markdown", "metadata": { "id": "MD80BKosH2bo" }, "source": [ "#####First example" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cwr3cUFXXhs4" }, "outputs": [], "source": [ "model=NEWmodel\n", "tokenizer=NEWtokenizer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fBLusTU5sId7" }, "outputs": [], "source": [ "abstract1=\"rokinase receptor : a molecular organizer in cellular communication .In a variety of cell types , the glycolipid - anchored urokinase receptor ( uPAR ) is colocalized pericellularly with components of the plasminogen activation system and endocytosis receptors . uPAR is also coexpressed with caveolin and members of the integrin adhesion receptor superfamily . The formation of functional units with these various proteins allows the uPAR to mediate the focused proteolysis required for cell migration and invasion and to contribute both directly and indirectly to cell adhesive processes in a non - proteolytic fashion .This dual activity , together with the initiation of signal transduction pathways by uPAR , is believed to influence cellular behaviour in angiogenesis , inflammation , wound repair and tumor progression / metastasis and open up the way for uPAR - based therapeutic approaches .\"\n", "\n", "#####Encoding######\n", "encode=tokenizer(abstract1,is_split_into_words=False)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "Mn903JVq60Bu", "outputId": "1ff9d5e7-2a87-4281-8562-ca952e148eb1" }, "outputs": [ { "data": { "text/plain": [ "{'input_ids': [101, 20996, 4939, 11022, 10769, 1024, 1037, 8382, 19012, 1999, 12562, 4807, 1012, 1999, 1037, 3528, 1997, 3526, 4127, 1010, 1996, 1043, 2135, 25778, 11514, 3593, 1011, 14453, 24471, 23212, 11649, 2063, 10769, 1006, 2039, 2906, 1007, 2003, 8902, 24755, 28931, 2566, 6610, 3363, 7934, 2135, 2007, 6177, 1997, 1996, 20228, 3022, 10020, 23924, 13791, 2291, 1998, 2203, 10085, 22123, 12650, 13833, 1012, 2039, 2906, 2003, 2036, 24873, 2595, 19811, 2007, 5430, 18861, 1998, 2372, 1997, 1996, 20014, 13910, 6657, 4748, 21471, 10769, 24169, 1012, 1996, 4195, 1997, 8360, 3197, 2007, 2122, 2536, 8171, 4473, 1996, 2039, 2906, 2000, 2865, 2618, 1996, 4208, 4013, 2618, 4747, 20960, 3223, 2005, 3526, 9230, 1998, 5274, 1998, 2000, 9002, 2119, 3495, 1998, 17351, 2000, 3526, 4748, 21579, 6194, 1999, 1037, 2512, 1011, 4013, 2618, 4747, 21252, 4827, 1012, 2023, 7037, 4023, 1010, 2362, 2007, 1996, 17890, 1997, 4742, 9099, 16256, 16910, 2011, 2039, 2906, 1010, 2003, 3373, 2000, 3747, 12562, 9164, 1999, 17076, 3695, 23737, 1010, 21733, 1010, 6357, 7192, 1998, 13656, 14967, 1013, 18804, 9153, 6190, 1998, 2330, 2039, 1996, 2126, 2005, 2039, 2906, 1011, 2241, 17261, 8107, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"AC with torch.no_grad():\n", " input_ids = torch.tensor(encode.input_ids).to(device)\n", " attention_mask = torch.tensor(encode.attention_mask).to(device)\n", " output = model(input_ids, attention_mask=attention_mask)\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5RG5cB0q91cH", "outputId": "3cb12148-16e8-4b55-d05a-098c9f8c972e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cuda\n" ] } ], "source": [ "mydevice = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", "print(mydevice)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NZTrTCi2-O9r" }, "outputs": [], "source": [ "#turn everything into pytorch tensors (don't forget to move to cuda...)\n", "enc_tensor={key:torch.tensor(value).to(mydevice) for key,value in encode.items()} #or simply specify in the previous line: return_tensors=\"pt\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yrQOBxGg79bI", "outputId": "2f0af86b-aac1-4e41-a657-78c94a7ac28d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cuda:0\n" ] } ], "source": [ "print(enc_tensor[\"input_ids\"].device)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iIcsyrYizBAo", "outputId": "edd910d4-d43d-4730-dca9-975f666e5975" }, "outputs": [ { "data": { "text/plain": [ "SequenceClassifierOutput(loss=None, logits=tensor([[ 1.7043, 3.3786, -1.8283, 4.4831, -0.1790, -3.0194, 2.7721, -2.4975,\n", " -4.7531, -3.3341, -2.1064, -5.0834, -3.4248, -5.3879]],\n", " device='cuda:0'), hidden_states=None, attentions=None)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with torch.no_grad(): #optional when doing inference, but it's recommended to do it in order to disable gradient computation to save memory and computation time\n", " output=model(enc_tensor[\"input_ids\"].unsqueeze(0),attention_mask=enc_tensor[\"attention_mask\"].unsqueeze(0))\n", "output" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hhfsGjB_2mux", "outputId": "3653c274-eef1-48ba-df5d-56693022ec8a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[0.8461, 0.9670, 0.1384, 0.9888, 0.4554, 0.0466, 0.9412, 0.0760, 0.0086,\n", " 0.0344, 0.1085, 0.0062, 0.0315, 0.0046]])\n", "[[1 1 0 1 0 0 1 0 0 0 0 0 0 0]]\n", "['A', 'B', 'D', 'G']\n", "['Anatomy', 'Organisms', 'Chemicals and Drugs', 'Phenomena and Processes']\n" ] } ], "source": [ "logits=output.logits\n", "probas=torch.sigmoid(logits.cpu())\n", "print(probas)\n", "pred=np.where(probas>=0.5,1,0)\n", "print(pred)\n", "\n", "pred_tags=[get_tag[i] for i,tag in enumerate(pred.tolist()[0]) if tag==1]\n", "print(pred_tags)\n", "interpreted_tags=[main_ref[tag] for tag in pred_tags]\n", "print(interpreted_tags)" ] }, { "cell_type": "markdown", "metadata": { "id": "gvn0lTLwD0Eo" }, "source": [ "#####Second example" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sWwPWQjEDbZH", "outputId": "40d207dd-6c1e-47fc-cb09-ece2624f7989" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[0.7674, 0.9700, 0.1592, 0.9937, 0.5032, 0.0169, 0.9591, 0.0516, 0.0065,\n", " 0.0300, 0.0977, 0.0090, 0.0354, 0.0054]])\n", "[[1 1 0 1 1 0 1 0 0 0 0 0 0 0]]\n", "['A', 'B', 'D', 'E', 'G']\n", "['Anatomy', 'Organisms', 'Chemicals and Drugs', 'Analytical, Diagnostic and Therapeutic Techniques, and Equipment', 'Phenomena and Processes']\n" ] } ], "source": [ "abstract2=\"Role of the interferon - inducible IFI16 gene in the induction of ICAM - 1 by TNF - alpha . The Interferon - inducible gene IFI16 , a member of the HIN200 family , is activated by oxidative stress and cell density , in addition to Interferons , and it is implicated in the regulation of endothelial cell proliferation and vessel formation in vitro . We have previously shown that IFI16 is required for proinflammatory gene stimulation by IFN - gamma through the NF - kappaB complex . To examine whether IFI16 induction might be extended to other proinflammatory cytokines such as tumor necrosis factor ( TNF ) - alpha , we used the strategy of the RNA interference to knock down IFI16 express i on , and analyze the capability of TNF - alpha to stimulate intercellular adhesion molecule - 1 ( ICAM - 1 or CD54 ) express i on in the absence of functional IFI16 . Our studies demonstrate that IFI16 mediates ICAM - 1 stimulation by TNF - alpha through the NF - kappaB pathway , thus reinforcing the role of the IFI16 molecule in the inflammation process .\"\n", "\n", "encode2=tokenizer(abstract2,is_split_into_words=False)\n", "enc_tensor2={key:torch.tensor(value).to(mydevice) for key,value in encode2.items()} \n", "\n", "with torch.no_grad():\n", " output2=model(enc_tensor2[\"input_ids\"].unsqueeze(0),attention_mask=enc_tensor2[\"attention_mask\"].unsqueeze(0))\n", "\n", "logits=output2.logits\n", "probas=torch.sigmoid(logits.cpu())\n", "print(probas)\n", "pred=np.where(probas>=0.5,1,0)\n", "print(pred)\n", "\n", "pred_tags=[get_tag[i] for i,tag in enumerate(pred.tolist()[0]) if tag==1]\n", "print(pred_tags)\n", "interpreted_tags=[main_ref[tag] for tag in pred_tags]\n", "print(interpreted_tags)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "B1tsx9n9D37q" }, "outputs": [], "source": [ "###END" ] } ], "metadata": { 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