7431 lines
268 KiB
Plaintext
7431 lines
268 KiB
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# RobertaForSequenceClassification model classification training"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Links:\n",
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"- Tensorboard training: https://tensorboard.dev/experiment/Hq95VFzqTQ2CyBb1S4SOpw/#scalars\n",
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"- Huggingface dataset edited: https://huggingface.co/Zombely/RobertaForSequenceClassification-sst2\n",
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"- Huggingface Trained model: https://huggingface.co/datasets/Zombely/sst2-project-dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "wLIiUmt93mn5",
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"outputId": "746cd310-e717-4757-ef01-217e6c27a16c"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\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[32m6.3/6.3 MB\u001b[0m \u001b[31m76.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m43.6 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",
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"\u001b[?25h"
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]
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}
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],
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"source": [
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"!pip install -q datasets transformers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "aAd7twoD3Fmj"
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},
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"outputs": [],
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"source": [
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"from datasets import load_dataset\n",
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"import torch\n",
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"from transformers import AutoTokenizer, RobertaForSequenceClassification, RobertaTokenizerFast, TrainingArguments, Trainer\n",
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"from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
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"from transformers.integrations import TensorBoardCallback"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "eedylD5a3IgG"
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},
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"outputs": [],
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"source": [
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"def load_and_process_dataset():\n",
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" dataset = load_dataset(\"sst2\")\n",
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" dataset.remove_columns('idx')\n",
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" del dataset['test']\n",
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" dataset['test'] = dataset['validation']\n",
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" del dataset['validation']\n",
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" split_dataset = dataset['train'].train_test_split(test_size=1600)\n",
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" dataset['train'] = split_dataset['train']\n",
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" dataset['validation'] = split_dataset['test']\n",
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" return dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 475,
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"referenced_widgets": [
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{
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{
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"text": [
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"Downloading and preparing dataset sst2/default to /root/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5...\n"
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"version_major": 2,
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"text": [
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"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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"DatasetDict({\n",
|
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" train: Dataset({\n",
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" features: ['idx', 'sentence', 'label'],\n",
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" num_rows: 65749\n",
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"8d3ef8711e6a4c428a971e36e8541ff7",
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"f5f9a67d99a7476d80d6143a1cf99c1e",
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"28127f0694be4016a784c7028a1ba72a",
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"83cffa91e4f54a9193a2eb8f56b64e39",
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"c3e35a50031e472c914c859912c6986e",
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"13aa5aba4d1646d9823c830d88e1a18f",
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"a0e790183d944b5c8d9bb9487ea04ad6",
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"1a21ee603d674c53a7e334f6163feac5",
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"a1e742789f454a3cadccbfda558e8ef5",
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"f126b8adf0b1461ba8f04df527435c83",
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"432de142d7d1492780c74371f7e5601f",
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"d968def823c94555b4cd6e7672a95948",
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"20c1dd7a357540a984e9b457993ac671",
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"480664d7794c4d938643c2d8ec9ee568",
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"841423f0963c47be95f7a0aa7dffceec",
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"8d46ea8015594452b8c95fa9fb69641d",
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"c63590dd011a49dd8f5934c214d83e62",
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|
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|
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"315c7d3456104cc1b212bf978184a808",
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"text": [
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|
"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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}
|
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],
|
|||
|
|
"source": [
|
|||
|
|
"model = RobertaForSequenceClassification.from_pretrained('roberta-base')\n",
|
|||
|
|
"tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length = 512)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
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{
|
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|
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|
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|
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|
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|
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|||
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|||
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|
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|
|||
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|
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|
|||
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|
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|
|||
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|
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|
|||
|
|
"45c53528d5764c2b92251488bb51ca78",
|
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"model_id": "8bf6224550b447bdadc6e41c29db317a",
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"source": [
|
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"# define a function that will tokenize the model, and will return the relevant inputs for the model\n",
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"def tokenization(batched_text):\n",
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" return tokenizer(batched_text['sentence'], padding = True, truncation=True)\n",
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"\n",
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"\n",
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"train_data = train.map(tokenization, batched = True, batch_size = len(train))\n",
|
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"val_data = validation.map(tokenization, batched = True, batch_size = len(validation))\n",
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"test_data = test.map(tokenization, batched = True, batch_size = len(test))"
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|
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"source": [
|
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"train_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])\n",
|
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|
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"val_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])\n",
|
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"test_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])"
|
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"outputs": [],
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"source": [
|
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"# define accuracy metrics\n",
|
|||
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"def compute_metrics(pred):\n",
|
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|
|
" labels = pred.label_ids\n",
|
|||
|
|
" preds = pred.predictions.argmax(-1)\n",
|
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|
|
" 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",
|
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|
|
" 'precision': precision,\n",
|
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" 'recall': recall\n",
|
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" }"
|
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|
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"cell_type": "code",
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"id": "03NpnRi33SHq"
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|
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"outputs": [],
|
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"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",
|
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|
|
" per_device_eval_batch_size= 8,\n",
|
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|
|
" evaluation_strategy = \"epoch\",\n",
|
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" disable_tqdm = False, \n",
|
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|
" load_best_model_at_end=False,\n",
|
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|
|
" warmup_steps=500,\n",
|
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|
|
" weight_decay=0.01,\n",
|
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|
|
" logging_steps = 8,\n",
|
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|
|
" fp16 = True,\n",
|
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|
|
" logging_dir='./logs',\n",
|
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|
|
" dataloader_num_workers = 2,\n",
|
|||
|
|
" run_name = 'roberta-classification',\n",
|
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|
|
" optim=\"adamw_torch\"\n",
|
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")"
|
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|
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|
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|
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|
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|
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|
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|
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|
|||
|
|
"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"
|
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|
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|
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|
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|
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"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",
|
|||
|
|
")"
|
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|
|
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|
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|
},
|
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{
|
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|
"cell_type": "code",
|
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|
|
"execution_count": 12,
|
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|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 1000
|
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|
|
},
|
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|
|
"id": "slHbKCSn3T-L",
|
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|
|
"outputId": "272cd7ec-b37f-45c3-fd3d-31ded2a401fc"
|
|||
|
|
},
|
|||
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|
"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"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
" <div>\n",
|
|||
|
|
" \n",
|
|||
|
|
" <progress value='3081' max='3081' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
|||
|
|
" [3081/3081 42:34, Epoch 2/3]\n",
|
|||
|
|
" </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",
|
|||
|
|
" <th>Precision</th>\n",
|
|||
|
|
" <th>Recall</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0.195700</td>\n",
|
|||
|
|
" <td>0.158939</td>\n",
|
|||
|
|
" <td>0.936250</td>\n",
|
|||
|
|
" <td>0.942761</td>\n",
|
|||
|
|
" <td>0.944882</td>\n",
|
|||
|
|
" <td>0.940649</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0.132900</td>\n",
|
|||
|
|
" <td>0.146519</td>\n",
|
|||
|
|
" <td>0.955000</td>\n",
|
|||
|
|
" <td>0.959551</td>\n",
|
|||
|
|
" <td>0.962796</td>\n",
|
|||
|
|
" <td>0.956327</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>0.039700</td>\n",
|
|||
|
|
" <td>0.150718</td>\n",
|
|||
|
|
" <td>0.955625</td>\n",
|
|||
|
|
" <td>0.960357</td>\n",
|
|||
|
|
" <td>0.957684</td>\n",
|
|||
|
|
" <td>0.963046</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table><p>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
"<IPython.core.display.HTML object>"
|
|||
|
|
]
|
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|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"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"
|
|||
|
|
]
|
|||
|
|
},
|
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|
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|
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|
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|
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" <div>\n",
|
|||
|
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" \n",
|
|||
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"{'eval_loss': 0.15071792900562286,\n",
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" 'eval_accuracy': 0.955625,\n",
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" 'eval_f1': 0.96035734226689,\n",
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" 'eval_precision': 0.9576837416481069,\n",
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" 'eval_recall': 0.9630459126539753,\n",
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" 'eval_runtime': 3.7924,\n",
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" 'eval_samples_per_second': 421.898,\n",
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" 'eval_steps_per_second': 52.737,\n",
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" 'epoch': 3.0}"
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|
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"output_type": "stream",
|
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"text": [
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|||
|
|
"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"
|
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"{'eval_loss': 0.20586328208446503,\n",
|
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" 'eval_accuracy': 0.9392201834862385,\n",
|
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|
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" 'eval_f1': 0.9407821229050279,\n",
|
|||
|
|
" 'eval_precision': 0.9334811529933481,\n",
|
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|
|
" 'eval_recall': 0.9481981981981982,\n",
|
|||
|
|
" 'eval_runtime': 2.3748,\n",
|
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|
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" 'eval_samples_per_second': 367.184,\n",
|
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" 'eval_steps_per_second': 45.898,\n",
|
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" 'epoch': 3.0}"
|
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|
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|
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|
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|
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|
|||
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|
],
|
|||
|
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"source": [
|
|||
|
|
"trainer.evaluate(test_data)"
|
|||
|
|
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|
|||
|
|
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|
|||
|
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|
|||
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|
|||
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|
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|
|||
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|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "hWZ0b-Zy4j_y",
|
|||
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"outputId": "ae7202b0-014d-4e12-b093-f494fe816493"
|
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|
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"outputs": [
|
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{
|
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"name": "stdout",
|
|||
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"output_type": "stream",
|
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"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",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
|
|||
|
|
" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
|
|||
|
|
" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
|
|||
|
|
" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
|
|||
|
|
" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\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"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"!huggingface-cli login"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 185,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"cbd11116207b486181c4c43979b9dec3",
|
|||
|
|
"831b6347e2e04644801fd735ff05ea0a",
|
|||
|
|
"d64fa193dc124a44aa4473eaf68a6fe0",
|
|||
|
|
"b9e21eeb459f44328539fb721769c9e1",
|
|||
|
|
"f778812398c64e5598ebc45e79a11c09",
|
|||
|
|
"1d3b54593d74415d82383336a8994c4e",
|
|||
|
|
"d1f7d3e50d06480da69bd565eac0381a",
|
|||
|
|
"391cf00a7b5943749ce0323917119a09",
|
|||
|
|
"1f57def550df4a5d82a42a7c39bc8005",
|
|||
|
|
"d3d4f102e8294de1a7c6d1696aa169ab",
|
|||
|
|
"8b9d548b971549fe85942c08e85b4183",
|
|||
|
|
"b50310cd013840fcbf73e09f07352d68",
|
|||
|
|
"8a336087b0f44ad09f70fe767be9e48e",
|
|||
|
|
"c5de2397a2584da093df0c260effb112",
|
|||
|
|
"79ed5ee25b0247998de475eeff22c328",
|
|||
|
|
"160f5a4de3f842368a6242dfb793077e",
|
|||
|
|
"41ed68bb08d34fdb8fe91b5e89cc1eac",
|
|||
|
|
"35d75260c1344c57a52102b72dcc4232",
|
|||
|
|
"dc97ee07af61405e87ae433006fae390",
|
|||
|
|
"019b00baaa27422e922fc689eb562fe9",
|
|||
|
|
"2a4128adfb314488af5230e76d05a88c",
|
|||
|
|
"62580a52989848c38235b4d17969cc83"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "-NBagdkY5RI1",
|
|||
|
|
"outputId": "7942fe7b-d998-4ce1-ba00-6bb94a3e294e"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"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"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "cbd11116207b486181c4c43979b9dec3",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
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