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--- |
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library_name: transformers |
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license: mit |
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base_model: roberta-base |
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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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- recall |
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- f1 |
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model-index: |
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- name: roberta-base-downstream-ildc |
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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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# roberta-base-downstream-ildc |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7039 |
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- Accuracy: 0.5030 |
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- Precision: 0.5015 |
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- Recall: 0.9960 |
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- F1: 0.6671 |
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- Best Threshold: 0.4007 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 1 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 32 |
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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: 20.0 |
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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 | Precision | Recall | F1 | Best Threshold | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------:| |
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| 0.6863 | 1.0 | 1010 | 0.7004 | 0.5111 | 0.5057 | 0.9859 | 0.6685 | 0.4378 | |
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| 0.6812 | 2.0 | 2020 | 0.6994 | 0.5030 | 0.5015 | 0.9960 | 0.6671 | 0.4333 | |
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| 0.6816 | 3.0 | 3030 | 0.7515 | 0.5030 | 0.5015 | 0.9839 | 0.6644 | 0.3329 | |
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| 0.6796 | 4.0 | 4040 | 0.7039 | 0.5030 | 0.5015 | 0.9960 | 0.6671 | 0.4007 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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