diff --git "a/train.ipynb" "b/train.ipynb" --- "a/train.ipynb" +++ "b/train.ipynb" @@ -3,21 +3,62 @@ { "cell_type": "code", "execution_count": 1, - "id": "1a08ff40", + "id": "8d07f027", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Using custom data configuration default-8bef3afded73c387\n", - "Reusing dataset json (/workspace/.cache/huggingface/datasets/json/default-8bef3afded73c387/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b)\n" + "Using custom data configuration default-8f1be135afea2a5e\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading and preparing dataset json/default to /workspace/.cache/huggingface/datasets/json/default-8f1be135afea2a5e/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1b68f3c845a14540b7fa4feb0b78d3e9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00\n", - " \n", + " \n", " Your browser does not support the audio element.\n", " \n", " " @@ -728,7 +849,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "1d66bd44", + "id": "5607b522", "metadata": { "id": "eJY7I0XAwe9p" }, @@ -749,7 +870,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "f5360bdd", + "id": "00e34422", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -786,7 +907,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c97a52c72198489f89a4481f722ac35a", + "model_id": "45634e805c76453a94ffac3f287df33f", "version_major": 2, "version_minor": 0 }, @@ -813,7 +934,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "5e8bb4ee", + "id": "4207ffd0", "metadata": { "id": "tborvC9hx88e" }, @@ -874,7 +995,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "98dfd52e", + "id": "2b346d91", "metadata": { "id": "lbQf5GuZyQ4_" }, @@ -886,7 +1007,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "5efc8697", + "id": "3afc8d2a", "metadata": { "id": "9Xsux2gmyXso" }, @@ -902,7 +1023,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "ec29ec29", + "id": "9119abc6", "metadata": { "id": "1XZ-kjweyTy_" }, @@ -927,7 +1048,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "d6d68f86", + "id": "172587ca", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -940,7 +1061,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_hid.weight', 'quantizer.weight_proj.bias', 'quantizer.codevectors', 'quantizer.weight_proj.weight', 'project_q.bias', 'project_hid.bias', 'project_q.weight']\n", + "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_hid.bias', 'project_q.weight', 'quantizer.weight_proj.bias', 'project_hid.weight', 'quantizer.weight_proj.weight', 'project_q.bias', 'quantizer.codevectors']\n", "- This IS expected if you are initializing Wav2Vec2ForCTC 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 Wav2Vec2ForCTC 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 Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n", @@ -955,7 +1076,7 @@ " #\"comodoro/wav2vec2-xls-r-300m-cs-cv8\", \n", " \"facebook/wav2vec2-xls-r-300m\", \n", " attention_dropout=0.1,\n", - " hidden_dropout=0.1,\n", + " hidden_dropout=0.2,\n", " feat_proj_dropout=0.0,\n", " mask_time_prob=0.1,\n", " layerdrop=0.1,\n", @@ -968,7 +1089,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "23d91592", + "id": "94625b79", "metadata": { "id": "oGI8zObtZ3V0" }, @@ -989,7 +1110,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "bf112a3a", + "id": "9c173ad4", "metadata": { "id": "KbeKSV7uzGPP" }, @@ -1000,18 +1121,18 @@ "training_args = TrainingArguments(\n", " output_dir=repo_name,\n", " group_by_length=True,\n", - " per_device_train_batch_size=16,\n", + " per_device_train_batch_size=32,\n", " gradient_accumulation_steps=1,\n", " eval_accumulation_steps=1,\n", " evaluation_strategy=\"steps\",\n", - " num_train_epochs=50,\n", + " num_train_epochs=5,\n", " gradient_checkpointing=True,\n", " fp16=True,\n", " save_steps=800,\n", " eval_steps=800,\n", " logging_steps=250,\n", - " learning_rate=1e-5,\n", - " warmup_steps=600,\n", + " learning_rate=1e-4,\n", + " warmup_steps=800,\n", " save_total_limit=2,\n", " report_to=\"tensorboard\"\n", ")" @@ -1020,7 +1141,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "6d209cae", + "id": "38cc611b", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1054,7 +1175,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "350ccf96", + "id": "ab7b22fa", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1073,11 +1194,11 @@ " warnings.warn(\n", "***** Running training *****\n", " Num examples = 159605\n", - " Num Epochs = 50\n", - " Instantaneous batch size per device = 16\n", - " Total train batch size (w. parallel, distributed & accumulation) = 16\n", + " Num Epochs = 5\n", + " Instantaneous batch size per device = 32\n", + " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 1\n", - " Total optimization steps = 498800\n" + " Total optimization steps = 24940\n" ] }, { @@ -1086,8 +1207,8 @@ "\n", "
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" @@ -1395,7 +1460,7 @@ "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-800/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-4000] due to args.save_total_limit\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-30400] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 7267\n", @@ -1404,7 +1469,7 @@ "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-1600/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-4800] due to args.save_total_limit\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-31200] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 7267\n", @@ -1666,101 +1731,22 @@ "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-24800/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-24800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-23200] due to args.save_total_limit\n", - "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", - "***** Running Evaluation *****\n", - " Num examples = 7267\n", - " Batch size = 8\n", - "Saving model checkpoint to wav2vec2-xls-r-300m-cs-250/checkpoint-25600\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-25600/config.json\n", - "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-25600/pytorch_model.bin\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-25600/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-24000] due to args.save_total_limit\n", - "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", - "***** Running Evaluation *****\n", - " Num examples = 7267\n", - " Batch size = 8\n", - "Saving model checkpoint to wav2vec2-xls-r-300m-cs-250/checkpoint-26400\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-26400/config.json\n", - "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-26400/pytorch_model.bin\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-26400/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-24800] due to args.save_total_limit\n", - "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", - "***** Running Evaluation *****\n", - " Num examples = 7267\n", - " Batch size = 8\n", - "Saving model checkpoint to wav2vec2-xls-r-300m-cs-250/checkpoint-27200\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-27200/config.json\n", - "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-27200/pytorch_model.bin\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-27200/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-25600] due to args.save_total_limit\n", - "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", - "***** Running Evaluation *****\n", - " Num examples = 7267\n", - " Batch size = 8\n", - "Saving model checkpoint to wav2vec2-xls-r-300m-cs-250/checkpoint-28000\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-28000/config.json\n", - "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-28000/pytorch_model.bin\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-28000/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-26400] due to args.save_total_limit\n", - "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", - "***** Running Evaluation *****\n", - " Num examples = 7267\n", - " Batch size = 8\n", - "Saving model checkpoint to wav2vec2-xls-r-300m-cs-250/checkpoint-28800\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-28800/config.json\n", - "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-28800/pytorch_model.bin\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-28800/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-27200] due to args.save_total_limit\n", - "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", - "***** Running Evaluation *****\n", - " Num examples = 7267\n", - " Batch size = 8\n", - "Saving model checkpoint to wav2vec2-xls-r-300m-cs-250/checkpoint-29600\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-29600/config.json\n", - "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-29600/pytorch_model.bin\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-29600/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-28000] due to args.save_total_limit\n", - "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", - "***** Running Evaluation *****\n", - " Num examples = 7267\n", - " Batch size = 8\n", - "Saving model checkpoint to wav2vec2-xls-r-300m-cs-250/checkpoint-30400\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-30400/config.json\n", - "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-30400/pytorch_model.bin\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-30400/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-28800] due to args.save_total_limit\n", - "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", - "***** Running Evaluation *****\n", - " Num examples = 7267\n", - " Batch size = 8\n", - "Saving model checkpoint to wav2vec2-xls-r-300m-cs-250/checkpoint-31200\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-31200/config.json\n", - "Model weights saved in wav2vec2-xls-r-300m-cs-250/checkpoint-31200/pytorch_model.bin\n", - "Configuration saved in wav2vec2-xls-r-300m-cs-250/checkpoint-31200/preprocessor_config.json\n", - "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-250/checkpoint-29600] due to args.save_total_limit\n" + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 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~/.local/lib/python3.8/site-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 1337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1338\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1339\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch_iterator\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[1;32m 1340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1341\u001b[0m \u001b[0;31m# Skip past any already trained steps if resuming training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 519\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\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--> 521\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\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[1;32m 522\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[1;32m 560\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 562\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 563\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\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[1;32m 48\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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 50\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 51\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1923\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# noqa: F811\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1924\u001b[0m \u001b[0;34m\"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1925\u001b[0;31m return self._getitem(\n\u001b[0m\u001b[1;32m 1926\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1927\u001b[0m )\n", - "\u001b[0;32m~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_getitem\u001b[0;34m(self, key, decoded, **kwargs)\u001b[0m\n\u001b[1;32m 1908\u001b[0m \u001b[0mformatter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_formatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecoded\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecoded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mformat_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1909\u001b[0m \u001b[0mpa_subtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_indices\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_indices\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1910\u001b[0;31m formatted_output = format_table(\n\u001b[0m\u001b[1;32m 1911\u001b[0m \u001b[0mpa_subtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mformat_columns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_all_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_all_columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1912\u001b[0m )\n", - 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] + "data": { + "text/plain": [ + "TrainOutput(global_step=24940, training_loss=0.7262697845434511, metrics={'train_runtime': 53649.1292, 'train_samples_per_second': 14.875, 'train_steps_per_second': 0.465, 'total_flos': 1.1982083586402645e+20, 'train_loss': 0.7262697845434511, 'epoch': 5.0})" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1769,26 +1755,46 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "6dad336a", + "execution_count": 33, + "id": "6610bd0a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Dropping the following result as it does not have all the necessary fields:\n", + "{}\n" + ] + } + ], "source": [ "trainer.create_model_card()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "ed1234c4", + "execution_count": 34, + "id": "ec5a5334", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Configuration saved in ./config.json\n", + "Model weights saved in ./pytorch_model.bin\n" + ] + } + ], + "source": [ + "model.save_pretrained('.')" + ] }, { "cell_type": "code", "execution_count": null, - "id": "f11836c9", + "id": "8d3d92c1", "metadata": {}, "outputs": [], "source": []