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du33169
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t5-base-finetuned-GLUE-MRPC

Safetensors
English
t5
Generated from Trainer
Eval Results
Model card Files Community
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  1. README.md +89 -0
  2. all_results.json +16 -0
  3. config.json +67 -0
  4. eval_results.json +11 -0
  5. model.safetensors +3 -0
  6. special_tokens_map.json +107 -0
  7. spiece.model +3 -0
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +937 -0
  10. train_results.json +8 -0
  11. trainer_state.json +166 -0
  12. training_args.bin +3 -0
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+ ---
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+ language:
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+ - en
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+ base_model: google-t5/t5-base
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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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+ - f1
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+ model-index:
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+ - name: MRPC
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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 MRPC
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+ type: glue
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+ args: mrpc
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8970588235294118
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+ - name: F1
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+ type: f1
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+ value: 0.926829268292683
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+ ---
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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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+
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+ # MRPC
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+
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+ This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the GLUE MRPC dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5629
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+ - Accuracy: 0.8971
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+ - F1: 0.9268
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+ - Combined Score: 0.9119
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Accuracy | Combined Score | F1 | Validation Loss |
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+ |:-------------:|:-----:|:----:|:--------:|:--------------:|:------:|:---------------:|
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+ | No log | 1.0 | 115 | 0.7108 | 0.7671 | 0.8234 | 0.5476 |
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+ | No log | 2.0 | 230 | 0.8701 | 0.8901 | 0.9100 | 0.3523 |
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+ | No log | 3.0 | 345 | 0.8725 | 0.8924 | 0.9122 | 0.3624 |
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+ | No log | 4.0 | 460 | 0.8775 | 0.8949 | 0.9123 | 0.3646 |
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+ | 0.3744 | 5.0 | 575 | 0.8946 | 0.9099 | 0.9252 | 0.4054 |
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+ | 0.3744 | 6.0 | 690 | 0.8897 | 0.9057 | 0.9217 | 0.4624 |
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+ | 0.3744 | 7.0 | 805 | 0.5530 | 0.8873 | 0.9212 | 0.9042 |
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+ | 0.3744 | 8.0 | 920 | 0.5405 | 0.8897 | 0.9220 | 0.9059 |
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+ | 0.0877 | 9.0 | 1035 | 0.5629 | 0.8971 | 0.9268 | 0.9119 |
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+ | 0.0877 | 10.0 | 1150 | 0.5856 | 0.8922 | 0.9241 | 0.9081 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.43.3
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+ - Pytorch 1.11.0+cu113
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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+ {
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+ "epoch": 10.0,
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+ "train_loss": 0.03282318198162577,
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+ "train_runtime": 93.6204,
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+ "train_samples": 3668,
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+ "train_samples_per_second": 391.795,
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+ "train_steps_per_second": 12.284
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+ }
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+ {
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+ "equivalent": 1,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "problem_type": "single_label_classification",
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "task_specific_params": {
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+ "summarization": {
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+ "num_beams": 4,
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+ }
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+ "torch_dtype": "float32",
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+ "use_cache": true,
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+ }
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