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--- |
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license: apache-2.0 |
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base_model: lxyuan/distilbert-base-multilingual-cased-sentiments-student |
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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-cased-finetuned-patient-doctor-text-classifier-eng-multilingual |
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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-cased-finetuned-patient-doctor-text-classifier-eng-multilingual |
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This model is a fine-tuned version of [lxyuan/distilbert-base-multilingual-cased-sentiments-student](https://huggingface.co/lxyuan/distilbert-base-multilingual-cased-sentiments-student) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0988 |
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- Accuracy: 0.9851 |
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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: 16 |
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- eval_batch_size: 16 |
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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 |
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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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| 0.1049 | 1.0 | 1547 | 0.0713 | 0.9819 | |
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| 0.0442 | 2.0 | 3094 | 0.0751 | 0.9833 | |
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| 0.0307 | 3.0 | 4641 | 0.0699 | 0.9856 | |
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| 0.0231 | 4.0 | 6188 | 0.0889 | 0.9844 | |
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| 0.0188 | 5.0 | 7735 | 0.0898 | 0.9852 | |
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| 0.0169 | 6.0 | 9282 | 0.0974 | 0.9831 | |
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| 0.0138 | 7.0 | 10829 | 0.0954 | 0.9852 | |
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| 0.0124 | 8.0 | 12376 | 0.0986 | 0.9845 | |
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| 0.0108 | 9.0 | 13923 | 0.0959 | 0.9846 | |
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| 0.0095 | 10.0 | 15470 | 0.0988 | 0.9851 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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