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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ag_news |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: results |
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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: ag_news |
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type: ag_news |
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config: default |
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split: train[:40000] |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8951 |
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- name: F1 |
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type: f1 |
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value: 0.8964447542636089 |
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- name: Precision |
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type: precision |
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value: 0.8978261707981314 |
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- name: Recall |
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type: recall |
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value: 0.896474840596734 |
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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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# results |
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the ag_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3320 |
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- Accuracy: 0.8951 |
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- F1: 0.8964 |
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- Precision: 0.8978 |
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- Recall: 0.8965 |
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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: 0.0003 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.2783 | 1.0 | 625 | 0.3046 | 0.8949 | 0.8960 | 0.8970 | 0.8963 | |
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| 0.1878 | 2.0 | 1250 | 0.3139 | 0.8954 | 0.8971 | 0.8995 | 0.8965 | |
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| 0.1311 | 3.0 | 1875 | 0.3320 | 0.8951 | 0.8964 | 0.8978 | 0.8965 | |
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### Framework versions |
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- Transformers 4.22.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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