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--- |
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library_name: transformers |
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license: agpl-3.0 |
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base_model: vinai/phobert-base-v2 |
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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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- precision |
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- recall |
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model-index: |
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- name: cita_test |
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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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# cita_test |
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This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3031 |
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- Accuracy: 0.9183 |
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- F1: 0.9029 |
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- Precision: 0.9113 |
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- Recall: 0.8958 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.6745 | 2.1277 | 100 | 0.2197 | 0.926 | 0.9110 | 0.9256 | 0.8996 | |
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| 0.2806 | 4.2553 | 200 | 0.2333 | 0.9293 | 0.9140 | 0.9363 | 0.8979 | |
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| 0.2806 | 6.3830 | 300 | 0.2366 | 0.916 | 0.9027 | 0.8998 | 0.9058 | |
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| 0.1669 | 8.5106 | 400 | 0.2277 | 0.9283 | 0.9149 | 0.9229 | 0.9080 | |
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| 0.1669 | 10.6383 | 500 | 0.2593 | 0.922 | 0.9067 | 0.9183 | 0.8972 | |
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| 0.1132 | 12.7660 | 600 | 0.2683 | 0.9243 | 0.9099 | 0.9191 | 0.9022 | |
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| 0.1132 | 14.8936 | 700 | 0.2796 | 0.9203 | 0.9051 | 0.9148 | 0.8969 | |
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| 0.0818 | 17.0213 | 800 | 0.2948 | 0.9217 | 0.9064 | 0.9175 | 0.8973 | |
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| 0.0818 | 19.1489 | 900 | 0.3031 | 0.9183 | 0.9029 | 0.9113 | 0.8958 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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