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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: verdict-classifier-trinary |
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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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# verdict-classifier-trinary |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1258 |
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- F1 Macro: 0.8408 |
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- F1 Misinformation: 0.9751 |
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- F1 Factual: 0.9508 |
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- F1 Other: 0.5965 |
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- Prec Macro: 0.8323 |
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- Prec Misinformation: 0.9818 |
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- Prec Factual: 1.0 |
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- Prec Other: 0.5152 |
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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: 4 |
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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_steps: 462 |
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- num_epochs: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:| |
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| 1.034 | 0.98 | 57 | 0.9960 | 0.3136 | 0.9408 | 0.0 | 0.0 | 0.2961 | 0.8882 | 0.0 | 0.0 | |
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| 0.968 | 1.98 | 114 | 0.8945 | 0.3136 | 0.9408 | 0.0 | 0.0 | 0.2961 | 0.8882 | 0.0 | 0.0 | |
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| 0.9253 | 2.98 | 171 | 0.7182 | 0.3136 | 0.9408 | 0.0 | 0.0 | 0.2961 | 0.8882 | 0.0 | 0.0 | |
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| 0.8215 | 3.98 | 228 | 0.3112 | 0.4795 | 0.9454 | 0.0 | 0.4932 | 0.4351 | 0.9381 | 0.0 | 0.3673 | |
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| 0.5073 | 4.98 | 285 | 0.1564 | 0.8272 | 0.9703 | 0.9355 | 0.5758 | 0.8025 | 0.9883 | 0.9667 | 0.4524 | |
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| 0.3046 | 5.98 | 342 | 0.1258 | 0.8408 | 0.9751 | 0.9508 | 0.5965 | 0.8323 | 0.9818 | 1.0 | 0.5152 | |
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| 0.1971 | 6.98 | 399 | 0.1540 | 0.8458 | 0.9796 | 0.9538 | 0.6038 | 0.8258 | 0.9863 | 0.9394 | 0.5517 | |
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| 0.1494 | 7.98 | 456 | 0.1779 | 0.8504 | 0.9737 | 0.9524 | 0.625 | 0.8195 | 0.9907 | 0.9677 | 0.5 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.9.0 |
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- Tokenizers 0.10.2 |
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