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--- |
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license: mit |
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base_model: FacebookAI/roberta-large |
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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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model-index: |
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- name: roberta-large-zeroshot-v2.0-2024-03-22-14-58 |
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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-large-zeroshot-v2.0-2024-03-22-14-58 |
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This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1357 |
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- F1 Macro: 0.5228 |
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- F1 Micro: 0.5294 |
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- Accuracy Balanced: 0.5577 |
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- Accuracy: 0.5294 |
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- Precision Macro: 0.6624 |
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- Recall Macro: 0.5577 |
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- Precision Micro: 0.5294 |
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- Recall Micro: 0.5294 |
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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: 9e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_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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- lr_scheduler_warmup_ratio: 0.06 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
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| 0.2319 | 1.0 | 27331 | 0.6148 | 0.7746 | 0.7904 | 0.7810 | 0.7904 | 0.7704 | 0.7810 | 0.7904 | 0.7904 | |
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| 0.1646 | 2.0 | 54662 | 0.6324 | 0.7822 | 0.7999 | 0.7847 | 0.7999 | 0.7800 | 0.7847 | 0.7999 | 0.7999 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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