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.outputs/outputs/README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+- f1
+model-index:
+- name: outputs
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE MRPC
+      type: glue
+      config: mrpc
+      split: validation
+      args: mrpc
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8406862745098039
+    - name: F1
+      type: f1
+      value: 0.888888888888889
+---
+
+<!-- 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. -->
+
+# outputs
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4183
+- Accuracy: 0.8407
+- F1: 0.8889
+- Combined Score: 0.8648
+
+## 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: 2e-05
+- train_batch_size: 32
+- 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 4.33.0.dev0
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.4
+- Tokenizers 0.13.3

.outputs/outputs/all_results.json

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+    "eval_accuracy": 0.8406862745098039,
+    "eval_combined_score": 0.8647875816993464,
+    "eval_f1": 0.888888888888889,
+    "eval_loss": 0.41829124093055725,
+    "eval_runtime": 56.9052,
+    "eval_samples": 408,
+    "eval_samples_per_second": 7.17,
+    "eval_steps_per_second": 0.896,
+    "train_loss": 0.428191568194956,
+    "train_runtime": 4927.0278,
+    "train_samples": 3668,
+    "train_samples_per_second": 2.233,
+    "train_steps_per_second": 0.07
+}

.outputs/outputs/config.json

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+{
+  "_name_or_path": "bert-base-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "mrpc",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "not_equivalent",
+    "1": "equivalent"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "equivalent": 1,
+    "not_equivalent": 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": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

.outputs/outputs/eval_results.json

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+{
+    "epoch": 3.0,
+    "eval_accuracy": 0.8406862745098039,
+    "eval_combined_score": 0.8647875816993464,
+    "eval_f1": 0.888888888888889,
+    "eval_loss": 0.41829124093055725,
+    "eval_runtime": 56.9052,
+    "eval_samples": 408,
+    "eval_samples_per_second": 7.17,
+    "eval_steps_per_second": 0.896
+}

.outputs/outputs/pytorch_model.bin

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.outputs/outputs/special_tokens_map.json

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

.outputs/outputs/tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
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+  "pad_token": "[PAD]",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

.outputs/outputs/train_results.json

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+}

.outputs/outputs/trainer_state.json

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