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--- |
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library_name: transformers |
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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: Bert-RAdam-Large |
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results: [] |
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datasets: |
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- surrey-nlp/PLOD-CW-25 |
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- surrey-nlp/PLODv2-filtered |
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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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# Bert-RAdam-Large |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a subset of the PLODv2-filtered dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2110 |
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- Precision: 0.7864 |
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- Recall: 0.8598 |
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- F1: 0.8215 |
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- Accuracy: 0.9403 |
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It achieves the following results on the test set: |
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- Loss: 0.1825 |
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- Precision: 0.8017 |
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- Recall: 0.8902 |
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- F1: 0.8436 |
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- Accuracy: 0.9500 |
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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: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.2244 | 1.0 | 500 | 0.1675 | 0.7653 | 0.8651 | 0.8121 | 0.9355 | |
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| 0.1231 | 2.0 | 1000 | 0.1673 | 0.7433 | 0.9011 | 0.8146 | 0.9375 | |
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| 0.0923 | 3.0 | 1500 | 0.1698 | 0.7867 | 0.8539 | 0.8189 | 0.9391 | |
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| 0.0657 | 4.0 | 2000 | 0.1865 | 0.7857 | 0.8405 | 0.8122 | 0.9394 | |
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| 0.0431 | 5.0 | 2500 | 0.2110 | 0.7864 | 0.8598 | 0.8215 | 0.9403 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.1 |
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- Tokenizers 0.21.1 |
