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README.md

@@ -1,3 +1,53 @@
 ---
 license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- bookcorpus
+model-index:
+- name: bookcorpus
+  results: []
 ---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bookcorpus
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the bookcorpus plain_text dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 10
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.22.0.dev0
+- Pytorch 1.12.1.post200
+- Datasets 2.4.0
+- Tokenizers 0.12.1

all_results.json

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+{
+    "epoch": 3.0,
+    "train_loss": 1.4489240925217872,
+    "train_runtime": 502024.9182,
+    "train_samples": 2277350,
+    "train_samples_per_second": 13.609,
+    "train_steps_per_second": 1.361
+}

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

plot.ipynb

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+   "id": "220eae6e",
+   "metadata": {},
+   "outputs": [
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+      "application/vnd.plotly.v1+json": {
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+        "plotlyServerURL": "https://plot.ly"
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+            "linecolor": "#A2B1C6",
+            "ticks": ""
+           },
+           "bgcolor": "white",
+           "caxis": {
+            "gridcolor": "#DFE8F3",
+            "linecolor": "#A2B1C6",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "#EBF0F8",
+           "linecolor": "#EBF0F8",
+           "ticks": "",
+           "title": {
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+           },
+           "zerolinecolor": "#EBF0F8",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
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+           },
+           "zerolinecolor": "#EBF0F8",
+           "zerolinewidth": 2
+          }
+         }
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+        },
+        "xaxis": {
+         "title": {
+          "text": "Epochs"
+         }
+        },
+        "yaxis": {
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+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import plotly.graph_objs as go\n",
+    "\n",
+    "df = pd.read_json(\"trainer_state.json\")\n",
+    "\n",
+    "df['loss_curve'] = df.log_history[:-1].apply(lambda d: d[\"loss\"])\n",
+    "df['epoch'] = df.log_history[:-1].apply(lambda d: d[\"epoch\"])\n",
+    "\n",
+    "df = df[['epoch', 'loss_curve']]\n",
+    "\n",
+    "trace = go.Scatter(x=df['epoch'], y=df['loss_curve'], \n",
+    "                    mode='lines', name='Loss', line=dict(color='blue', width=2))\n",
+    "\n",
+    "layout = go.Layout(title=f'Loss Curve for BERT-base (bookcorpus)', \n",
+    "                    xaxis=dict(title='Epochs'), \n",
+    "                    yaxis=dict(title='Loss'), \n",
+    "                    template='plotly_white')\n",
+    "\n",
+    "fig = go.Figure(data=[trace], layout=layout)\n",
+    "fig.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pytorch_model.bin

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special_tokens_map.json

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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "bert-base-uncased",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

train_results.json

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+{
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+    "train_loss": 1.4489240925217872,
+    "train_runtime": 502024.9182,
+    "train_samples": 2277350,
+    "train_samples_per_second": 13.609,
+    "train_steps_per_second": 1.361
+}

training_args.bin

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