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--- |
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license: mit |
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base_model: FacebookAI/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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model-index: |
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- name: roberta-best-finetuned-hopes-fears |
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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-best-finetuned-hopes-fears |
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3593 |
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- Accuracy: 0.9434 |
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- F1 Weighted: 0.9453 |
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- Precision Fears: 0.7053 |
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- Recall Fears: 0.8171 |
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- F1 Fears: 0.7571 |
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- Precision Hopes: 0.7458 |
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- Recall Hopes: 0.88 |
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- F1 Hopes: 0.8073 |
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- Precision Neither: 0.9795 |
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- Recall Neither: 0.9579 |
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- F1 Neither: 0.9685 |
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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: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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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: 600 |
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- num_epochs: 5 |
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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 | F1 Weighted | Precision Fears | Recall Fears | F1 Fears | Precision Hopes | Recall Hopes | F1 Hopes | Precision Neither | Recall Neither | F1 Neither | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:-----------------:|:--------------:|:----------:| |
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| No log | 1.0 | 214 | 0.7739 | 0.8930 | 0.8651 | 0.4776 | 0.2602 | 0.3368 | 0.0 | 0.0 | 0.0 | 0.9129 | 0.9876 | 0.9488 | |
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| 0.8895 | 2.0 | 428 | 0.2800 | 0.8960 | 0.9087 | 0.4736 | 0.9106 | 0.6231 | 0.7417 | 0.89 | 0.8091 | 0.9893 | 0.8949 | 0.9397 | |
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| 0.2905 | 3.0 | 642 | 0.3252 | 0.9492 | 0.9496 | 0.7879 | 0.7398 | 0.7631 | 0.7143 | 0.95 | 0.8155 | 0.9759 | 0.9691 | 0.9725 | |
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| 0.2905 | 4.0 | 856 | 0.2671 | 0.9281 | 0.9340 | 0.5813 | 0.8862 | 0.7021 | 0.8018 | 0.89 | 0.8436 | 0.9869 | 0.9335 | 0.9595 | |
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| 0.1741 | 5.0 | 1070 | 0.3593 | 0.9434 | 0.9453 | 0.7053 | 0.8171 | 0.7571 | 0.7458 | 0.88 | 0.8073 | 0.9795 | 0.9579 | 0.9685 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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