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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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- f1 |
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model-index: |
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- name: pharma_classification |
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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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# pharma_classification |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5315 |
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- Accuracy: 0.9581 |
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- F1: 0.9506 |
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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: 8 |
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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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- training_steps: 30000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
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| 0.0035 | 5.99 | 5000 | 0.2892 | 0.9539 | 0.9554 | |
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| 0.0137 | 11.98 | 10000 | 0.2620 | 0.9641 | 0.9600 | |
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| 0.0 | 17.96 | 15000 | 0.4022 | 0.9611 | 0.9586 | |
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| 0.0001 | 23.95 | 20000 | 0.3838 | 0.9611 | 0.9552 | |
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| 0.0 | 29.94 | 25000 | 0.4363 | 0.9575 | 0.9490 | |
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| 0.0 | 35.93 | 30000 | 0.5315 | 0.9581 | 0.9506 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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