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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilbert-base-uncased-finetuned-clinc |
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results: [] |
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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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# distilbert-base-uncased-finetuned-clinc |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Accuracy: 0.9410 |
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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: 2e-05 |
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- train_batch_size: 192 |
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- eval_batch_size: 192 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 80 | nan | 0.4606 | |
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| No log | 2.0 | 160 | nan | 0.7219 | |
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| No log | 3.0 | 240 | nan | 0.8071 | |
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| No log | 4.0 | 320 | nan | 0.8626 | |
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| No log | 5.0 | 400 | nan | 0.8858 | |
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| No log | 6.0 | 480 | nan | 0.9103 | |
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| 0.0 | 7.0 | 560 | nan | 0.9216 | |
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| 0.0 | 8.0 | 640 | nan | 0.9268 | |
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| 0.0 | 9.0 | 720 | nan | 0.9294 | |
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| 0.0 | 10.0 | 800 | nan | 0.9345 | |
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| 0.0 | 11.0 | 880 | nan | 0.9345 | |
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| 0.0 | 12.0 | 960 | nan | 0.9387 | |
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| 0.0 | 13.0 | 1040 | nan | 0.9390 | |
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| 0.0 | 14.0 | 1120 | nan | 0.9403 | |
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| 0.0 | 15.0 | 1200 | nan | 0.9419 | |
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| 0.0 | 16.0 | 1280 | nan | 0.9406 | |
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| 0.0 | 17.0 | 1360 | nan | 0.94 | |
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| 0.0 | 18.0 | 1440 | nan | 0.9403 | |
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| 0.0 | 19.0 | 1520 | nan | 0.9413 | |
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| 0.0 | 20.0 | 1600 | nan | 0.9410 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.2 |
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