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uczenie_glebokie_projekt/RobertaSequanceClassification.ipynb
Michał Kozłowski e457ab5cee init
2023-02-13 14:22:50 +01:00

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# RobertaForSequenceClassification model classification training"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Links:\n",
"- Tensorboard training: https://tensorboard.dev/experiment/Hq95VFzqTQ2CyBb1S4SOpw/#scalars\n",
"- Huggingface dataset edited: https://huggingface.co/Zombely/RobertaForSequenceClassification-sst2\n",
"- Huggingface Trained model: https://huggingface.co/datasets/Zombely/sst2-project-dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wLIiUmt93mn5",
"outputId": "746cd310-e717-4757-ef01-217e6c27a16c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install -q datasets transformers"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "aAd7twoD3Fmj"
},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"import torch\n",
"from transformers import AutoTokenizer, RobertaForSequenceClassification, RobertaTokenizerFast, TrainingArguments, Trainer\n",
"from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
"from transformers.integrations import TensorBoardCallback"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "eedylD5a3IgG"
},
"outputs": [],
"source": [
"def load_and_process_dataset():\n",
" dataset = load_dataset(\"sst2\")\n",
" dataset.remove_columns('idx')\n",
" del dataset['test']\n",
" dataset['test'] = dataset['validation']\n",
" del dataset['validation']\n",
" split_dataset = dataset['train'].train_test_split(test_size=1600)\n",
" dataset['train'] = split_dataset['train']\n",
" dataset['validation'] = split_dataset['test']\n",
" return dataset"
]
},
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{
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"text/plain": [
"Downloading builder script: 0%| | 0.00/3.77k [00:00<?, ?B/s]"
]
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{
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"Downloading readme: 0%| | 0.00/5.06k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset sst2/default to /root/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5...\n"
]
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"Downloading data: 0%| | 0.00/7.44M [00:00<?, ?B/s]"
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{
"data": {
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},
"text/plain": [
"Generating train split: 0%| | 0/67349 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"Generating validation split: 0%| | 0/872 [00:00<?, ? examples/s]"
]
},
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{
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"Generating test split: 0%| | 0/1821 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset sst2 downloaded and prepared to /root/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5. Subsequent calls will reuse this data.\n"
]
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"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['idx', 'sentence', 'label'],\n",
" num_rows: 65749\n",
" })\n",
" test: Dataset({\n",
" features: ['idx', 'sentence', 'label'],\n",
" num_rows: 872\n",
" })\n",
" validation: Dataset({\n",
" features: ['idx', 'sentence', 'label'],\n",
" num_rows: 1600\n",
" })\n",
"})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = load_and_process_dataset()\n",
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "XFHi6yjg3MWf"
},
"outputs": [],
"source": [
"train = dataset['train']\n",
"validation = dataset['validation']\n",
"test = dataset['test']"
]
},
{
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{
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"version_major": 2,
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"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/481 [00:00<?, ?B/s]"
]
},
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},
{
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"text/plain": [
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},
"metadata": {},
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},
{
"name": "stderr",
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"text": [
"Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.bias', 'roberta.pooler.dense.weight', 'lm_head.bias', 'roberta.pooler.dense.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias']\n",
"- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\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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]
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"source": [
"model = RobertaForSequenceClassification.from_pretrained('roberta-base')\n",
"tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length = 512)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
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{
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],
"source": [
"# define a function that will tokenize the model, and will return the relevant inputs for the model\n",
"def tokenization(batched_text):\n",
" return tokenizer(batched_text['sentence'], padding = True, truncation=True)\n",
"\n",
"\n",
"train_data = train.map(tokenization, batched = True, batch_size = len(train))\n",
"val_data = validation.map(tokenization, batched = True, batch_size = len(validation))\n",
"test_data = test.map(tokenization, batched = True, batch_size = len(test))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "HsRTQvCg3P8B"
},
"outputs": [],
"source": [
"train_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])\n",
"val_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])\n",
"test_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "LmjL41Ar3RGs"
},
"outputs": [],
"source": [
"# define accuracy metrics\n",
"def compute_metrics(pred):\n",
" labels = pred.label_ids\n",
" preds = pred.predictions.argmax(-1)\n",
" precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')\n",
" acc = accuracy_score(labels, preds)\n",
" return {\n",
" 'accuracy': acc,\n",
" 'f1': f1,\n",
" 'precision': precision,\n",
" 'recall': recall\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "03NpnRi33SHq"
},
"outputs": [],
"source": [
"# define the training arguments\n",
"training_args = TrainingArguments(\n",
" output_dir = './results',\n",
" num_train_epochs=3,\n",
" per_device_train_batch_size = 4,\n",
" gradient_accumulation_steps = 16, \n",
" per_device_eval_batch_size= 8,\n",
" evaluation_strategy = \"epoch\",\n",
" disable_tqdm = False, \n",
" load_best_model_at_end=False,\n",
" warmup_steps=500,\n",
" weight_decay=0.01,\n",
" logging_steps = 8,\n",
" fp16 = True,\n",
" logging_dir='./logs',\n",
" dataloader_num_workers = 2,\n",
" run_name = 'roberta-classification',\n",
" optim=\"adamw_torch\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YUR7Pay63TLa",
"outputId": "beead4dc-403a-46be-98d1-5033d2f7641f"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You are adding a <class 'transformers.integrations.TensorBoardCallback'> to the callbacks of this Trainer, but there is already one. The currentlist of callbacks is\n",
":DefaultFlowCallback\n",
"TensorBoardCallback\n",
"Using cuda_amp half precision backend\n"
]
}
],
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" compute_metrics=compute_metrics,\n",
" train_dataset=train_data,\n",
" eval_dataset=val_data,\n",
" callbacks=[TensorBoardCallback]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "slHbKCSn3T-L",
"outputId": "272cd7ec-b37f-45c3-fd3d-31ded2a401fc"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following columns in the training set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"***** Running training *****\n",
" Num examples = 65749\n",
" Num Epochs = 3\n",
" Instantaneous batch size per device = 4\n",
" Total train batch size (w. parallel, distributed & accumulation) = 64\n",
" Gradient Accumulation steps = 16\n",
" Total optimization steps = 3081\n",
" Number of trainable parameters = 124647170\n"
]
},
{
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" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Accuracy</th>\n",
" <th>F1</th>\n",
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"text": [
"Saving model checkpoint to ./results/checkpoint-500\n",
"Configuration saved in ./results/checkpoint-500/config.json\n",
"Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n",
"Saving model checkpoint to ./results/checkpoint-1000\n",
"Configuration saved in ./results/checkpoint-1000/config.json\n",
"Model weights saved in ./results/checkpoint-1000/pytorch_model.bin\n",
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 1600\n",
" Batch size = 8\n",
"Saving model checkpoint to ./results/checkpoint-1500\n",
"Configuration saved in ./results/checkpoint-1500/config.json\n",
"Model weights saved in ./results/checkpoint-1500/pytorch_model.bin\n",
"Saving model checkpoint to ./results/checkpoint-2000\n",
"Configuration saved in ./results/checkpoint-2000/config.json\n",
"Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n",
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 1600\n",
" Batch size = 8\n",
"Saving model checkpoint to ./results/checkpoint-2500\n",
"Configuration saved in ./results/checkpoint-2500/config.json\n",
"Model weights saved in ./results/checkpoint-2500/pytorch_model.bin\n",
"Saving model checkpoint to ./results/checkpoint-3000\n",
"Configuration saved in ./results/checkpoint-3000/config.json\n",
"Model weights saved in ./results/checkpoint-3000/pytorch_model.bin\n",
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 1600\n",
" Batch size = 8\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n"
]
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=3081, training_loss=0.19893329894531087, metrics={'train_runtime': 2559.2258, 'train_samples_per_second': 77.073, 'train_steps_per_second': 1.204, 'total_flos': 6790599311126760.0, 'train_loss': 0.19893329894531087, 'epoch': 3.0})"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "pA_VW8AZG2su",
"outputId": "d8f02cb1-fe12-4f73-eb87-dd24b7f496f2"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 1600\n",
" Batch size = 8\n"
]
},
{
"data": {
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"{'eval_loss': 0.15071792900562286,\n",
" 'eval_accuracy': 0.955625,\n",
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" 'eval_runtime': 3.7924,\n",
" 'eval_samples_per_second': 421.898,\n",
" 'eval_steps_per_second': 52.737,\n",
" 'epoch': 3.0}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.evaluate()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "dp1nwOYcG8iy",
"outputId": "1415db6a-8a8d-4420-abd0-2d24c9e93a26"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 872\n",
" Batch size = 8\n"
]
},
{
"data": {
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"{'eval_loss': 0.20586328208446503,\n",
" 'eval_accuracy': 0.9392201834862385,\n",
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" 'eval_precision': 0.9334811529933481,\n",
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" 'epoch': 3.0}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.evaluate(test_data)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hWZ0b-Zy4j_y",
"outputId": "ae7202b0-014d-4e12-b093-f494fe816493"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-02-13 13:06:49.916239: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
"2023-02-13 13:06:49.916330: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
"2023-02-13 13:06:49.916358: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
"\n",
"***** TensorBoard Uploader *****\n",
"\n",
"This will upload your TensorBoard logs to https://tensorboard.dev/ from\n",
"the following directory:\n",
"\n",
"logs\n",
"\n",
"This TensorBoard will be visible to everyone. Do not upload sensitive\n",
"data.\n",
"\n",
"Your use of this service is subject to Google's Terms of Service\n",
"<https://policies.google.com/terms> and Privacy Policy\n",
"<https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service\n",
"<https://tensorboard.dev/policy/terms/>.\n",
"\n",
"This notice will not be shown again while you are logged into the uploader.\n",
"To log out, run `tensorboard dev auth revoke`.\n",
"\n",
"Continue? (yes/NO) yes\n",
"\n",
"To sign in with the TensorBoard uploader:\n",
"\n",
"1. On your computer or phone, visit:\n",
"\n",
" https://www.google.com/device\n",
"\n",
"2. Sign in with your Google account, then enter:\n",
"\n",
" YBVF-QCSV\n",
"\n",
"\n",
"Upload started and will continue reading any new data as it's added to the logdir.\n",
"\n",
"To stop uploading, press Ctrl-C.\n",
"\n",
"New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/Hq95VFzqTQ2CyBb1S4SOpw/\n",
"\n",
"\u001b[1m[2023-02-13T13:07:05]\u001b[0m Started scanning logdir.\n",
"\u001b[1m[2023-02-13T13:07:06]\u001b[0m Total uploaded: 2412 scalars, 10 tensors (7.1 kB), 0 binary objects\n",
"\n",
"\n",
"Interrupted. View your TensorBoard at https://tensorboard.dev/experiment/Hq95VFzqTQ2CyBb1S4SOpw/\n",
"Traceback (most recent call last):\n",
" File \"/usr/local/bin/tensorboard\", line 8, in <module>\n",
" sys.exit(run_main())\n",
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/main.py\", line 46, in run_main\n",
" app.run(tensorboard.main, flags_parser=tensorboard.configure)\n",
" File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 308, in run\n",
" _run_main(main, args)\n",
" File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 254, in _run_main\n",
" sys.exit(main(argv))\n",
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/program.py\", line 276, in main\n",
" return runner(self.flags) or 0\n",
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 691, in run\n",
" return _run(flags, self._experiment_url_callback)\n",
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 124, in _run\n",
" intent.execute(server_info, channel)\n",
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 507, in execute\n",
" sys.stdout.write(end_message + \"\\n\")\n",
"KeyboardInterrupt\n",
"^C\n"
]
}
],
"source": [
"!tensorboard dev upload --logdir logs --name RobertaForSequenceClassification"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "x-_gsbS34_Mf",
"outputId": "ef2463c6-dc96-4af3-f752-c7f46d7f57ef"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Configuration saved in ./model/config.json\n",
"Model weights saved in ./model/pytorch_model.bin\n"
]
}
],
"source": [
"model.save_pretrained(\"./model\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gOeREu-e5IlE",
"outputId": "8415481a-0350-4433-b111-906d871a086f"
},
"outputs": [
{
"name": "stdout",
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"text": [
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" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
" \n",
" To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
"Token: \n",
"Add token as git credential? (Y/n) y\n",
"Token is valid.\n",
"\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
"You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
"Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
"\n",
"git config --global credential.helper store\n",
"\n",
"Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
"Token has not been saved to git credential helper.\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful\n"
]
}
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"Configuration saved in /tmp/tmpga7eb38a/config.json\n",
"Model weights saved in /tmp/tmpga7eb38a/pytorch_model.bin\n",
"Uploading the following files to Zombely/RobertaForSequenceClassification-sst2: config.json,pytorch_model.bin\n"
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" adding: model/ (stored 0%)\n",
" adding: model/config.json (deflated 51%)\n",
" adding: model/pytorch_model.bin (deflated 12%)\n"
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