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--- |
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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: phobert-base-v2-finetuned-cola |
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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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# phobert-base-v2-finetuned-cola |
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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: 0.4604 |
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- Accuracy: 0.9018 |
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- F1: 0.9034 |
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- Precision: 0.9080 |
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- Recall: 0.9018 |
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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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- 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 | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 39 | 1.1866 | 0.8474 | 0.8536 | 0.8922 | 0.8474 | |
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| No log | 2.0 | 78 | 0.8260 | 0.8632 | 0.8683 | 0.8967 | 0.8632 | |
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| No log | 3.0 | 117 | 0.4604 | 0.9018 | 0.9034 | 0.9080 | 0.9018 | |
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| No log | 4.0 | 156 | 0.6405 | 0.8912 | 0.8927 | 0.8962 | 0.8912 | |
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| No log | 5.0 | 195 | 0.6415 | 0.8895 | 0.8909 | 0.8941 | 0.8895 | |
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| No log | 6.0 | 234 | 0.6742 | 0.9053 | 0.9074 | 0.9157 | 0.9053 | |
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| No log | 7.0 | 273 | 0.8472 | 0.8719 | 0.8762 | 0.8971 | 0.8719 | |
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| No log | 8.0 | 312 | 0.7390 | 0.8947 | 0.8975 | 0.9086 | 0.8947 | |
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| No log | 9.0 | 351 | 0.7700 | 0.8930 | 0.8958 | 0.9074 | 0.8930 | |
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| No log | 10.0 | 390 | 0.7635 | 0.8930 | 0.8958 | 0.9074 | 0.8930 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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