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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_uncased_L-4_H-128_A-2 |
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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: bert_uncased_L-4_H-128_A-2_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: 0.7573529411764706 |
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- name: F1 |
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type: f1 |
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value: 0.840064620355412 |
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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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# bert_uncased_L-4_H-128_A-2_mrpc |
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This model is a fine-tuned version of [google/bert_uncased_L-4_H-128_A-2](https://huggingface.co./google/bert_uncased_L-4_H-128_A-2) on the GLUE MRPC dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5327 |
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- Accuracy: 0.7574 |
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- F1: 0.8401 |
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- Combined Score: 0.7987 |
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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: 256 |
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- eval_batch_size: 256 |
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- seed: 10 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| |
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| 0.6437 | 1.0 | 15 | 0.6181 | 0.6838 | 0.8122 | 0.7480 | |
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| 0.6197 | 2.0 | 30 | 0.6047 | 0.6912 | 0.8158 | 0.7535 | |
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| 0.595 | 3.0 | 45 | 0.5877 | 0.6985 | 0.8161 | 0.7573 | |
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| 0.582 | 4.0 | 60 | 0.5687 | 0.7279 | 0.8284 | 0.7782 | |
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| 0.5617 | 5.0 | 75 | 0.5594 | 0.7279 | 0.8295 | 0.7787 | |
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| 0.5409 | 6.0 | 90 | 0.5550 | 0.7132 | 0.8208 | 0.7670 | |
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| 0.5213 | 7.0 | 105 | 0.5417 | 0.7255 | 0.8245 | 0.7750 | |
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| 0.4968 | 8.0 | 120 | 0.5530 | 0.7328 | 0.8310 | 0.7819 | |
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| 0.4741 | 9.0 | 135 | 0.5580 | 0.7353 | 0.8333 | 0.7843 | |
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| 0.4545 | 10.0 | 150 | 0.5390 | 0.7549 | 0.8397 | 0.7973 | |
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| 0.4366 | 11.0 | 165 | 0.5327 | 0.7574 | 0.8401 | 0.7987 | |
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| 0.4206 | 12.0 | 180 | 0.5350 | 0.7598 | 0.8424 | 0.8011 | |
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| 0.397 | 13.0 | 195 | 0.5649 | 0.7549 | 0.8447 | 0.7998 | |
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| 0.3873 | 14.0 | 210 | 0.5602 | 0.7623 | 0.8482 | 0.8052 | |
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| 0.3725 | 15.0 | 225 | 0.5622 | 0.7525 | 0.8399 | 0.7962 | |
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| 0.3506 | 16.0 | 240 | 0.5588 | 0.7525 | 0.8374 | 0.7949 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.20.3 |
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