Delete rte
Browse files- rte/README.md +0 -73
- rte/all_results.json +0 -15
- rte/config.json +0 -32
- rte/eval_results.json +0 -9
- rte/model.safetensors +0 -3
- rte/runs/May15_00-21-03_cs-Precision-7960-Tower/events.out.tfevents.1747282869.cs-Precision-7960-Tower.139999.0 +0 -3
- rte/runs/May15_00-21-03_cs-Precision-7960-Tower/events.out.tfevents.1747282940.cs-Precision-7960-Tower.139999.1 +0 -3
- rte/special_tokens_map.json +0 -7
- rte/tokenizer.json +0 -0
- rte/tokenizer_config.json +0 -56
- rte/train_results.json +0 -9
- rte/trainer_state.json +0 -42
- rte/training_args.bin +0 -3
- rte/vocab.txt +0 -0
    	
        rte/README.md
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            ---
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            library_name: transformers
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            language:
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            - en
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            license: apache-2.0
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            base_model: google-bert/bert-base-cased
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            tags:
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            - generated_from_trainer
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            datasets:
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            - glue
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            metrics:
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            - accuracy
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            model-index:
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            - name: rte
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              results:
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              - task:
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                  name: Text Classification
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                  type: text-classification
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                dataset:
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                  name: GLUE RTE
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                  type: glue
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                  args: rte
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                metrics:
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                - name: Accuracy
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                  type: accuracy
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                  value: 0.7256317689530686
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            ---
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            <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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            should probably proofread and complete it, then remove this comment. -->
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            # rte
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            This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the GLUE RTE dataset.
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            It achieves the following results on the evaluation set:
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            - Loss: 1.1735
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            - Accuracy: 0.7256
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            ## Model description
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            More information needed
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            ## Intended uses & limitations
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            More information needed
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            ## Training and evaluation data
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            More information needed
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            ## Training procedure
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            ### Training hyperparameters
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            The following hyperparameters were used during training:
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            - learning_rate: 5e-05
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            - train_batch_size: 32
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            - eval_batch_size: 8
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            - seed: 42
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            - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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            - lr_scheduler_type: linear
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            - num_epochs: 5.0
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            ### Training results
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            ### Framework versions
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            - Transformers 4.49.0
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            - Pytorch 2.6.0+cu118
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            - Datasets 3.3.1
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            - Tokenizers 0.21.0
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            {
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                "epoch": 5.0,
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                "eval_accuracy": 0.7256317689530686,
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            {
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              "_name_or_path": "google-bert/bert-base-cased",
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              "architectures": [
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                "BertForSequenceClassification"
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              ],
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              "attention_probs_dropout_prob": 0.1,
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              "classifier_dropout": null,
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              "finetuning_task": "rte",
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              "gradient_checkpointing": false,
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              "hidden_act": "gelu",
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              "hidden_dropout_prob": 0.1,
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              "hidden_size": 768,
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              "initializer_range": 0.02,
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              "intermediate_size": 3072,
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              "label2id": {
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                "entailment": 0,
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                "not_entailment": 1
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              },
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              "layer_norm_eps": 1e-12,
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              "max_position_embeddings": 512,
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              "model_type": "bert",
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              "num_attention_heads": 12,
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              "num_hidden_layers": 12,
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              "pad_token_id": 0,
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              "position_embedding_type": "absolute",
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              "problem_type": "single_label_classification",
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              "torch_dtype": "float32",
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              "transformers_version": "4.49.0",
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              "type_vocab_size": 2,
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              "use_cache": true,
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              "vocab_size": 28996
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            }
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        rte/eval_results.json
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        rte/model.safetensors
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