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alexcpn/gpt2_overfitting.ipynb

Created November 28, 2024 13:08
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gpt2_overfitting.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/alexcpn/08379e788288f7bda20f58eede116231/gpt2_overfitting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vNSFcDPBBXy7"
},
"outputs": [],
"source": [
"!pip install transformers"
]
},
{
"cell_type": "code",
"source": [
"#upload files to your colab environment\n",
"!wget https://raw.githubusercontent.com/alexcpn/tranformer_learn/main/data/small_3.txt"
],
"metadata": {
"id": "zLV1aqxZDI8O"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train_path = 'small_3.txt'"
],
"metadata": {
"id": "wpwb0BeyDWo8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers import TextDataset,DataCollatorForLanguageModeling\n",
"from transformers import AutoTokenizer\n",
"\n",
"def load_dataset(path,tokenizer):\n",
" dataset = TextDataset(\n",
" tokenizer=tokenizer,\n",
" file_path=path,\n",
" block_size=128)\n",
"\n",
" data_collator = DataCollatorForLanguageModeling(\n",
" tokenizer=tokenizer, mlm=False,\n",
" )\n",
" return dataset,data_collator\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"gpt2-medium\")\n",
"train_dataset,data_collator = load_dataset(train_path,tokenizer)"
],
"metadata": {
"id": "l1-dtEE0Ecbp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
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"\n",
"model = AutoModelWithLMHead.from_pretrained(\"gpt2-medium\")\n",
"\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./gpt2-small3-v2\", #The output directory\n",
" overwrite_output_dir=True, #overwrite the content of the output directory\n",
" num_train_epochs=50, # number of training epochs\n",
" per_device_train_batch_size=2, # batch size for training\n",
" per_device_eval_batch_size=2, # batch size for evaluation\n",
" eval_steps = 400, # Number of update steps between two evaluations.\n",
" save_steps=800, # after # steps model is saved\n",
" warmup_steps=500,# number of warmup steps for learning rate scheduler\n",
" prediction_loss_only=True,\n",
" )\n",
"\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=data_collator,\n",
" train_dataset=train_dataset,\n",
" #eval_dataset=test_dataset,\n",
")"
],
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"base_uri": "https://localhost:8080/",
"height": 136,
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"f5dadf6ba21841c88176b05c9b5f0b3a",
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"6c28d890d016424bbf4293fab5598762"
]
},
"id": "2d6QgbMrG4yD",
"outputId": "4519b788-c44d-43b3-d0f6-2066ad5aabd8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/transformers/models/auto/modeling_auto.py:1322: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading pytorch_model.bin: 0%| | 0.00/1.52G [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "476b8f8100c74112b3602b85c35a4635"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)neration_config.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "95a0332b78ba4b24918224511e39ea96"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 518
},
"id": "psbZA3YMHF2z",
"outputId": "78bd7e87-d409-4796-929b-fb156ea17f5a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1655' max='2350' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1655/2350 7:15:15 < 3:03:00, 0.06 it/s, Epoch 35.19/50]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>2.263500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>0.205800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>0.029900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.9/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 1660\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 1661\u001b[0m )\n\u001b[0;32m-> 1662\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 1663\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 1664\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1927\u001b[0m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1928\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1929\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1931\u001b[0m if (\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2698\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2699\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2701\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m 2729\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2730\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2731\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2732\u001b[0m \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2733\u001b[0m \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1073\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_return_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1074\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1075\u001b[0;31m transformer_outputs = self.transformer(\n\u001b[0m\u001b[1;32m 1076\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1077\u001b[0m \u001b[0mpast_key_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpast_key_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 897\u001b[0m )\n\u001b[1;32m 898\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 899\u001b[0;31m outputs = block(\n\u001b[0m\u001b[1;32m 900\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 901\u001b[0m \u001b[0mlayer_past\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayer_past\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0mresidual\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mln_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 426\u001b[0m \u001b[0mfeed_forward_hidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0;31m# residual connection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/normalization.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m return F.layer_norm(\n\u001b[0m\u001b[1;32m 191\u001b[0m input, self.normalized_shape, self.weight, self.bias, self.eps)\n\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlayer_norm\u001b[0;34m(input, normalized_shape, weight, bias, eps)\u001b[0m\n\u001b[1;32m 2513\u001b[0m \u001b[0mlayer_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalized_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2514\u001b[0m )\n\u001b[0;32m-> 2515\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_norm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalized_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcudnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menabled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2516\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
]
},
{
"cell_type": "markdown",
"source": [
"\n",
"Short detail about training -\n",
"- Step\tTraining Loss\n",
"- 500\t2.263500\n",
"- 1000\t0.205800\n",
"- 1500\t0.029900\n",
"\n",
"Took about 6 hours in Colab Free in TPU runtime\n",
"\n",
"Stopped at Epoch 35 as the Training loss was pretty low\n",
"\n",
"1655/2350 7:15:15 < 3:03:00, 0.06 it/s, Epoch 35.19/50]"
],
"metadata": {
"id": "kavOmpTpVHy5"
}
},
{
"cell_type": "code",
"source": [
"trainer.save_model()"
],
"metadata": {
"id": "aVehIp-JHwsO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers import pipeline\n",
"\n",
"test = pipeline('text-generation',model='./gpt2-small3-v2/checkpoint-1600/', tokenizer='gpt2')"
],
"metadata": {
"id": "XN-eoDuiHXPz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"88b1693b19964b6dba2a44aac9deb486",
"5b32c3f39f244edcbd672752abbdd1ef",
"f977a40a238946d7a63d2c711abacf10",
"96b003de07e048f882fc259d655d9c50",
"32b243056d734f74b60662e57df4a9f3",
"be705f5a3fda416884d45f20d8bd2cc5",
"84cd36c050864e1babc7dbc3775b1600",
"ff1c4273dfb444a584ccb61e63130c65",
"1e9700ef2e934c24abc5f9abf3f28dbc",
"d2eadba9954e4c6bb30f569fdb780b1b",
"c3c1906b241d42fd97359a61f7fa9b71",
"462e6d450816472babf29c83178273fc",
"9b25e6c947e14890bf7efbf8fee788ed",
"594ccf73466d41efb05cbcc3feb92ede",
"5d3fa2243feb4737b38c11e2f426aaf5",
"e8c0dcd9d110416eb179e3e0f8055d3e",
"9eeaf9b179ae42cfb217917525949e20",
"1ae9d0394b4245d2974a91bc1bd4073d",
"2661a33e69ec49cc82c472954c850080",
"2210d6ac6ff54f34b7c01e0545fbc539",
"e0215224b98540108d5961f04eb15ce6",
"b3b7c7cf2633454baadb78f39d61608a",
"ef9f0c2654fe40d6990b44445485687c",
"986130e9eaaf461087b3a21b47c1aed2",
"8df2ddbb527146a69e78013721c757d4",
"c17b040925674e939a9f146f04bc21be",
"b5f8abdc2d854e4f96ec445169f2cfea",
"e8b7b620edaa41359b3d6216fbac7d8b",
"d9783981efc34e56aaa4c1eef959e68e",
"e9e349e08393452ab790018b5571f966",
"7794bbb938da411da5595a09ee10bc67",
"ab871188c9274000ba234b2e0f45b5ce",
"84b40283702a4cbfb2009dd20828c941",
"f8e6e764f825423dbdc3caefb9b43036",
"a386cad17a3e4c05ba943d47587f6df1",
"c5eafd425e1b487b80e80c87d523602e",
"1ae0b6c58ad94968b9072ccdf522dc87",
"c3cee713ac1c405fa2261645d7223a1c",
"673c5962e5ff4c83ac52e3e6d45171e4",
"151a63ec9d094cedb54ee1181dc37f84",
"1cf5a98ad2404889af003bc6d0bb58c0",
"e595b66e210b469dbfc477962d2963fb",
"b0e7e02ba36a41659298009713768da4",
"4fa27ca1fcfa4093bea801714ee5853c"
]
},
"outputId": "1df8f046-adb5-488b-e7c9-30f66b41786e"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/665 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "88b1693b19964b6dba2a44aac9deb486"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)olve/main/vocab.json: 0%| | 0.00/1.04M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "462e6d450816472babf29c83178273fc"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)olve/main/merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ef9f0c2654fe40d6990b44445485687c"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)/main/tokenizer.json: 0%| | 0.00/1.36M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f8e6e764f825423dbdc3caefb9b43036"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"test('An alkaline medium favours', max_new_tokens=100)"
],
"metadata": {
"id": "CKyPR4inIg9f",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2fcad631-c088-463e-948a-053bb5550ce9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/transformers/generation/utils.py:1219: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
" warnings.warn(\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'An alkaline medium favours bacterial growth; and moisture is a necessary condition; spores, however, can survive the want of water for much longer periods than fully developed bacteria. The necessity for oxygen varies in different species. Those that require oxygen are known as aërobic bacilli or aërobes ; those that cannot live in the presence of oxygen are spoken of as anaërobes. The great majority of bacteria, however, while they prefer to have oxygen, are able to live without'}]"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"source": [
"This is as expected from the passage\n",
"[ An alkaline medium favours bacterial growth; and moisture is a necessary condition; spores, however, can survive the want of water for much longer periods than fully developed bacteria. The necessity for oxygen varies in different species. Those that require oxygen are known as aërobic bacilli or aërobes ; those that cannot live in the presence of oxygen are spoken of as anaërobes . The great majority of bacteria, however, while they prefer to have oxygen, are able to live without it, and are called facultative anaërobes"
],
"metadata": {
"id": "OAFPCf2kUgEO"
}
},
{
"cell_type": "code",
"source": [
"test('Streptococci are met with in', max_new_tokens=100)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B5fhnJ8XFQ3Y",
"outputId": "a37b5ad4-399c-4f04-ef56-6ede5bea5c53"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'Streptococci are met with in erysipelas and various other inflammatory and suppurative processes of a spreading character. Bacilli are rod-shaped bacteria, usually at least twice as long as they are broad (Fig. 36). Some multiply by fission, others by sporulation. Some forms are motile, others are non-motile. Some forms are motile only in virtue of the contractility of the protoplasm, some in virtue of the fibroblasts which they carry. Others are'}]"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "markdown",
"source": [
"The inital part is exaclty as in the passage [Streptococci are met with in erysipelas and various other inflammatory and suppurative processes of a spreading character. Bacilli are rod-shaped bacteria, usually at least twice as long as they are broad (Fig. 4). Some multiply by fission, others by sporulation. Some forms are motile, others are non-motile.]\n",
"\n",
"The last line\n",
"\n",
"from passage [Tuberculosis, tetanus, anthrax, and many other surgical diseases are due to different forms of bacilli. Spirilla are long, slender, thread-like cells, more or less spiral or wavy.]\n",
"\n",
"The last line generated is not correct\n",
"\n",
"\"Some forms are motile only in virtue of the contractility of the protoplasm, \"\n",
"\n",
"is there in another pace where \"motile\" is reffered\n",
"\n",
"whereas the following is pure hallicunation\n",
"\n",
"\"some in virtue of the fibroblasts which they carry. Others are'}]\""
],
"metadata": {
"id": "ZD9lCgUOW_qy"
}
},
{
"cell_type": "code",
"source": [
"test('What is Streptococci', max_new_tokens=100)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SsN0r9eeYUgJ",
"outputId": "2f1e33f0-567d-417c-cb4d-02ddc393a76e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'What is Streptococci? Streptococci are long, slender, thread-like cells, more or less spiral or wavy. Some move by a screw-like contraction of the protoplasm, some by flagellainternalisation. The spirochate associated with syphilis (Fig. 36) is the most important member of this group. Clinical Use of Streptococci. When a new disease is brought about by the action of an invading micro-organism, the initial shock of'}]"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"test('Streptococci', max_new_tokens=100)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kvae0bUIZevB",
"outputId": "f2d7ca60-4988-4554-9364-5b8b06b6c6f2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'Streptococci are met with in erysipel Legs and Upper Cutaneous Tissue. Bacilli are rod-shaped bacteria, usually at least twice as long as they are broad (Fig. 5Hypothesis 1). Some multiply by fission, others by sporulation. Some forms are motile, others are non-motile. Some forms are motile by the introduction of spores, which are produced in the interior of the growing medium. Spores are remarkable for their tenacity'}]"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
" test('John Hilton\\'s classical work', max_new_tokens=100)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MhURBC-XZvfQ",
"outputId": "165d8555-9d6e-427d-d5f2-ebb9c27822c4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'John Hilton\\'s classical work, \"The Blood,\" which appeared in London in 1755, is the most important work of recent writers on the subject of blood. In it he distinguishes between the normal corpuscles, which are derived from the proliferating cells of the part, and the specialised corpuscles, which are derived from the surviving cells of the part. The great majority of writers do not recognise these varieties of corpuscles as distinct from one another, but as forms of temporary life-support for the developing cells of the'}]"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
" test('Who was Ghandi', max_new_tokens=100)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lS7zht1daNtu",
"outputId": "ff743c0c-09c5-4834-c244-87f35fb08404"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'Who was Ghandi? A short biography of the great humanist, written by A. E. Wright, Esq. It would appear that the germ theory of disease is derived from the fact that certain pathological conditions, such as those of high blood pressure, diabetes, syphilis, scurvy, or alcoholism, sometimes present themselves in the course of old age, when the vitality of the tissues is low, and when the individual is in a normal health. The theory is advanced by A. E. Wright'}]"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
" test('skin grafting', max_new_tokens=100)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hb5E3Nquaq4U",
"outputId": "4759bdf8-ce30-4c65-cdce-e328b824d55f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'skin grafting is carried out when the surface is covered by a uniform layer of healthy granulations and before the inevitable contraction of scar tissue makes itself manifest. Before applying the grafts it is usual to scrape away the granulations until the young fibrous tissue underneath is exposed, but, if the granulations are healthy and can be rendered aseptic, the grafts may be placed on them directly. If it is decided to scrape away the granulations, the oozing must be arrested by pressure with a pad'}]"
]
},
"metadata": {},
"execution_count": 18
}
]
}
]
}
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