Training in progress, epoch 0

.ipynb_checkpoints/finetuning_text_classification-checkpoint.ipynb

@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d090c366-23e5-4221-a868-f290eefcedc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"google/boolq\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6bad310-9514-4468-bdca-673b30dfd473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "tokenizer=AutoTokenizer.from_pretrained(\"bert-base-uncased\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "013559ce-c991-4836-922c-5f9201265c66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38aac997-3d15-4e61-b80c-c1a4fff0b525",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset[\"train\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4d214cd-2fef-4778-bc3a-cb4e1c907515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode_question_context_pairs(example):\n",
+    "    text=f'{example[\"question\"]} [SEP] {example[\"passage\"]}'\n",
+    "    label= 0 if not example[\"answer\"] else 1\n",
+    "    inputs=tokenizer(text,truncation=True)\n",
+    "    inputs[\"labels\"]=[float(label)]\n",
+    "    return inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fa2aa41-6286-4a69-ba23-90482d98f494",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataset=dataset[\"train\"].map(encode_question_context_pairs,remove_columns=dataset[\"train\"].column_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "309bee55-b698-4c66-990d-beb00ac52746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_dataset=dataset[\"validation\"].map(encode_question_context_pairs,remove_columns=dataset[\"train\"].column_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf95690a-4ed4-4635-9b39-12bc4b486b5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# train_dataset['labels']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00c07517-6976-4553-8188-2b7f4078adf3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1371cc4a-3f0e-4e84-939b-218b570c0b6b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85c9ccea-f788-4025-b185-c32c6fa51c46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tokenizer(\"question\",\"answer\",max_length=512,padding=\"max_length\",truncation=\"only_second\",)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30a82635-f956-404d-a95e-db753f7e07b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import DataCollatorWithPadding\n",
+    "\n",
+    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22d43e81-1739-443f-95fb-ee98b10a3a0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import evaluate\n",
+    "\n",
+    "accuracy = evaluate.load(\"accuracy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23fa9362-aa3d-4155-85a5-6caa6635c9f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def compute_metrics(eval_pred):\n",
+    "    predictions, labels = eval_pred\n",
+    "    predictions = np.where(predictions<0.5,0,1)\n",
+    "    return accuracy.compute(predictions=predictions, references=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e476c76f-21b6-4844-a6a5-29f18b4f6099",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
+    "\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    \"bert-base-uncased\", num_labels=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a359a0d-7563-4f4e-b4d4-03e6c601fc2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_args = TrainingArguments(\n",
+    "    output_dir=\"./\",\n",
+    "    learning_rate=2e-5,\n",
+    "    per_device_train_batch_size=16,\n",
+    "    per_device_eval_batch_size=16,\n",
+    "    num_train_epochs=4,\n",
+    "    weight_decay=0.01,\n",
+    "    evaluation_strategy=\"epoch\",\n",
+    "    save_strategy=\"epoch\",\n",
+    "    load_best_model_at_end=True,\n",
+    "    gradient_accumulation_steps=4,\n",
+    "    logging_steps=50,\n",
+    "    seed=42,\n",
+    "    adam_beta1= 0.9,\n",
+    "    adam_beta2= 0.999,\n",
+    "    adam_epsilon= 1e-08,\n",
+    "    report_to=\"tensorboard\",\n",
+    "    push_to_hub=True,\n",
+    ")\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=train_dataset,\n",
+    "    eval_dataset=validation_dataset,\n",
+    "    tokenizer=tokenizer,\n",
+    "    data_collator=data_collator,\n",
+    "    compute_metrics=compute_metrics,\n",
+    ")\n",
+    "\n",
+    "# trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bc0fca5-d298-40d3-a80b-035a05fe6e1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save_pretrained(training_args.output_dir)\n",
+    "tokenizer.save_pretrained(training_args.output_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c96926e2-04c1-4e33-b83f-dc2b9c4d5b08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75e96eb2-0d8e-4e5f-8844-6abce16bd1cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kwargs = {\n",
+    "    \"dataset_tags\": \"google/boolq\",\n",
+    "    \"dataset\": \"boolq\",  # a 'pretty' name for the training dataset\n",
+    "    \"language\": \"en\",\n",
+    "    \"model_name\": \"Bert Base Uncased Boolean Question Answer model\",  # a 'pretty' name for your model\n",
+    "    \"finetuned_from\": \"bert-base-uncased\",\n",
+    "    \"tasks\": \"text-classification\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba5e73bd-d154-43ce-a869-f0f57045a386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(**kwargs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

config.json

@@ -0,0 +1,33 @@
+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "regression",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

finetuning_text_classification.ipynb

@@ -0,0 +1,382 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d090c366-23e5-4221-a868-f290eefcedc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"google/boolq\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a6bad310-9514-4468-bdca-673b30dfd473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "tokenizer=AutoTokenizer.from_pretrained(\"bert-base-uncased\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "013559ce-c991-4836-922c-5f9201265c66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['question', 'answer', 'passage'],\n",
+       "        num_rows: 9427\n",
+       "    })\n",
+       "    validation: Dataset({\n",
+       "        features: ['question', 'answer', 'passage'],\n",
+       "        num_rows: 3270\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "38aac997-3d15-4e61-b80c-c1a4fff0b525",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'question': 'do iran and afghanistan speak the same language',\n",
+       " 'answer': True,\n",
+       " 'passage': 'Persian (/ˈpɜːrʒən, -ʃən/), also known by its endonym Farsi (فارسی fārsi (fɒːɾˈsiː) ( listen)), is one of the Western Iranian languages within the Indo-Iranian branch of the Indo-European language family. It is primarily spoken in Iran, Afghanistan (officially known as Dari since 1958), and Tajikistan (officially known as Tajiki since the Soviet era), and some other regions which historically were Persianate societies and considered part of Greater Iran. It is written in the Persian alphabet, a modified variant of the Arabic script, which itself evolved from the Aramaic alphabet.'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset[\"train\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f4d214cd-2fef-4778-bc3a-cb4e1c907515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode_question_context_pairs(example):\n",
+    "    text=f'{example[\"question\"]} [SEP] {example[\"passage\"]}'\n",
+    "    label= 0 if not example[\"answer\"] else 1\n",
+    "    inputs=tokenizer(text,truncation=True)\n",
+    "    inputs[\"labels\"]=[float(label)]\n",
+    "    return inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6fa2aa41-6286-4a69-ba23-90482d98f494",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataset=dataset[\"train\"].map(encode_question_context_pairs,remove_columns=dataset[\"train\"].column_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "309bee55-b698-4c66-990d-beb00ac52746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_dataset=dataset[\"validation\"].map(encode_question_context_pairs,remove_columns=dataset[\"train\"].column_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bf95690a-4ed4-4635-9b39-12bc4b486b5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# train_dataset['labels']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00c07517-6976-4553-8188-2b7f4078adf3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1371cc4a-3f0e-4e84-939b-218b570c0b6b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "85c9ccea-f788-4025-b185-c32c6fa51c46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tokenizer(\"question\",\"answer\",max_length=512,padding=\"max_length\",truncation=\"only_second\",)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "30a82635-f956-404d-a95e-db753f7e07b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import DataCollatorWithPadding\n",
+    "\n",
+    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "22d43e81-1739-443f-95fb-ee98b10a3a0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import evaluate\n",
+    "\n",
+    "accuracy = evaluate.load(\"accuracy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "23fa9362-aa3d-4155-85a5-6caa6635c9f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def compute_metrics(eval_pred):\n",
+    "    predictions, labels = eval_pred\n",
+    "    predictions = np.where(predictions<0.5,0,1)\n",
+    "    return accuracy.compute(predictions=predictions, references=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e476c76f-21b6-4844-a6a5-29f18b4f6099",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
+    "\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    \"bert-base-uncased\", num_labels=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5a359a0d-7563-4f4e-b4d4-03e6c601fc2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_args = TrainingArguments(\n",
+    "    output_dir=\"./\",\n",
+    "    learning_rate=2e-5,\n",
+    "    per_device_train_batch_size=16,\n",
+    "    per_device_eval_batch_size=16,\n",
+    "    num_train_epochs=4,\n",
+    "    weight_decay=0.01,\n",
+    "    evaluation_strategy=\"epoch\",\n",
+    "    save_strategy=\"epoch\",\n",
+    "    load_best_model_at_end=True,\n",
+    "    gradient_accumulation_steps=4,\n",
+    "    logging_steps=50,\n",
+    "    seed=42,\n",
+    "    adam_beta1= 0.9,\n",
+    "    adam_beta2= 0.999,\n",
+    "    adam_epsilon= 1e-08,\n",
+    "    report_to=\"tensorboard\",\n",
+    "    push_to_hub=True,\n",
+    ")\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=train_dataset,\n",
+    "    eval_dataset=validation_dataset,\n",
+    "    tokenizer=tokenizer,\n",
+    "    data_collator=data_collator,\n",
+    "    compute_metrics=compute_metrics,\n",
+    ")\n",
+    "\n",
+    "# trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0bc0fca5-d298-40d3-a80b-035a05fe6e1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('./tokenizer_config.json',\n",
+       " './special_tokens_map.json',\n",
+       " './vocab.txt',\n",
+       " './added_tokens.json',\n",
+       " './tokenizer.json')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.save_pretrained(training_args.output_dir)\n",
+    "tokenizer.save_pretrained(training_args.output_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c96926e2-04c1-4e33-b83f-dc2b9c4d5b08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='148' max='588' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [148/588 07:00 < 21:07, 0.35 it/s, Epoch 1.00/4]\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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table><p>\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='102' max='205' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [102/205 00:26 < 00:27, 3.76 it/s]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75e96eb2-0d8e-4e5f-8844-6abce16bd1cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kwargs = {\n",
+    "    \"dataset_tags\": \"google/boolq\",\n",
+    "    \"dataset\": \"boolq\",  # a 'pretty' name for the training dataset\n",
+    "    \"language\": \"en\",\n",
+    "    \"model_name\": \"Bert Base Uncased Boolean Question Answer model\",  # a 'pretty' name for your model\n",
+    "    \"finetuned_from\": \"bert-base-uncased\",\n",
+    "    \"tasks\": \"text-classification\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba5e73bd-d154-43ce-a869-f0f57045a386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(**kwargs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf66bfdcb9c367e6e70099af4ba926f4e7636c1562231ef2f272ec545f4ebc75
+size 437955572

runs/Apr20_13-50-06_386b24d31d4c/events.out.tfevents.1713621007.386b24d31d4c

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c9560a1d93a3251de9c8ec432f164a5da072e66ab34c30898a5b5dfe5f7f181
+size 4693

runs/Apr20_13-51-30_386b24d31d4c/events.out.tfevents.1713621092.386b24d31d4c

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ecca68f93aaf9ce7b644564aec6d87f2caec4cefe1abc0129e39ec387ebe0c5
+size 4693

runs/Apr20_13-54-34_386b24d31d4c/events.out.tfevents.1713621277.386b24d31d4c

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:381504dba9f24f32546e28f5aeacbe7e397bbbb4b156fb4f62aa4ef5f0457e2b
+size 5430

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:772843bd8850c3c86ff160e8c7e9457e2b3e5a7bf7bf27f2b2a6453662366a70
+size 4984