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README.md

@@ -1,3 +1,53 @@
 ---
 license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- wikitext
+model-index:
+- name: wikitext
+  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. -->
+
+# wikitext
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the wikitext wikitext-103-raw-v1 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": 1.5861018672255278,
+    "train_runtime": 49915.9647,
+    "train_samples": 228639,
+    "train_samples_per_second": 13.741,
+    "train_steps_per_second": 1.374
+}

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
+}

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+    "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 (wikitext)', \n",
+    "                    xaxis=dict(title='Epochs'), \n",
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+    "fig = go.Figure(data=[trace], layout=layout)\n",
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special_tokens_map.json

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tokenizer_config.json

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train_results.json

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