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--- |
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license: apache-2.0 |
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base_model: bert-base-cased |
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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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- precision |
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- f1 |
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- recall |
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model-index: |
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- name: newsdata-bert |
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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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# newsdata-bert |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4835 |
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- Accuracy: 0.8617 |
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- Precision: 0.8494 |
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- F1: 0.8533 |
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- Recall: 0.8617 |
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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: 4 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.2095 | 0.1024 | 1000 | 1.0182 | 0.7335 | 0.6811 | 0.6915 | 0.7335 | |
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| 0.8995 | 0.2048 | 2000 | 0.8102 | 0.7798 | 0.7622 | 0.7586 | 0.7798 | |
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| 0.7554 | 0.3071 | 3000 | 0.6720 | 0.8165 | 0.7938 | 0.8023 | 0.8165 | |
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| 0.6805 | 0.4095 | 4000 | 0.6185 | 0.828 | 0.8107 | 0.8157 | 0.828 | |
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| 0.6192 | 0.5119 | 5000 | 0.5865 | 0.8322 | 0.8233 | 0.8226 | 0.8322 | |
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| 0.5963 | 0.6143 | 6000 | 0.5462 | 0.8475 | 0.8333 | 0.8356 | 0.8475 | |
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| 0.5466 | 0.7166 | 7000 | 0.5384 | 0.849 | 0.8386 | 0.8398 | 0.849 | |
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| 0.5447 | 0.8190 | 8000 | 0.4923 | 0.8582 | 0.8440 | 0.8491 | 0.8582 | |
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| 0.5288 | 0.9214 | 9000 | 0.4835 | 0.8617 | 0.8494 | 0.8533 | 0.8617 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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