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roberta-best-finetuned-hopes-fears

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End of training

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  1. README.md +20 -25
  2. model.safetensors +1 -1
  3. runs/Jun03_09-22-20_ec6854305179/events.out.tfevents.1717406548.ec6854305179.4427.0 +3 -0
  4. runs/Jun03_09-22-42_ec6854305179/events.out.tfevents.1717406565.ec6854305179.4427.1 +3 -0
  5. tokenizer.json +14 -2
  6. training_args.bin +1 -1
README.md CHANGED
@@ -17,18 +17,18 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.6120
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- - Accuracy: 0.9496
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- - F1 Weighted: 0.9509
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- - Precision Hopes: 0.6757
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- - Recall Hopes: 0.7622
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- - F1 Hopes: 0.7163
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- - Precision Fears: 0.8672
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- - Recall Fears: 0.9328
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- - F1 Fears: 0.8988
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- - Precision Neither: 0.9798
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- - Recall Neither: 0.9655
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- - F1 Neither: 0.9726
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  ## Model description
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@@ -54,28 +54,23 @@ The following hyperparameters were used during training:
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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: 10
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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 Hopes | Recall Hopes | F1 Hopes | Precision Fears | Recall Fears | F1 Fears | Precision Neither | Recall Neither | F1 Neither |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:-----------------:|:--------------:|:----------:|
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- | No log | 1.0 | 171 | 1.0201 | 0.8791 | 0.8226 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8791 | 1.0 | 0.9357 |
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- | 0.9075 | 2.0 | 342 | 0.3018 | 0.9146 | 0.9220 | 0.5251 | 0.8293 | 0.6430 | 0.7020 | 0.8908 | 0.7852 | 0.9834 | 0.9227 | 0.9521 |
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- | 0.9075 | 3.0 | 513 | 0.2450 | 0.9141 | 0.9229 | 0.4947 | 0.8476 | 0.6247 | 0.7724 | 0.9412 | 0.8485 | 0.9864 | 0.9179 | 0.9509 |
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- | 0.2782 | 4.0 | 684 | 0.3093 | 0.9402 | 0.9436 | 0.6 | 0.8232 | 0.6941 | 0.9182 | 0.8487 | 0.8821 | 0.9796 | 0.9548 | 0.9670 |
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- | 0.2782 | 5.0 | 855 | 0.2838 | 0.9030 | 0.9145 | 0.4744 | 0.9024 | 0.6218 | 0.6970 | 0.9664 | 0.8099 | 0.9930 | 0.8994 | 0.9439 |
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- | 0.1561 | 6.0 | 1026 | 0.4644 | 0.9479 | 0.9495 | 0.6548 | 0.7866 | 0.7147 | 0.9273 | 0.8571 | 0.8908 | 0.9774 | 0.9660 | 0.9717 |
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- | 0.1561 | 7.0 | 1197 | 0.4976 | 0.9496 | 0.9512 | 0.6788 | 0.7988 | 0.7339 | 0.8516 | 0.9160 | 0.8826 | 0.9817 | 0.9636 | 0.9725 |
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- | 0.0743 | 8.0 | 1368 | 0.5949 | 0.9517 | 0.9528 | 0.6978 | 0.7744 | 0.7341 | 0.8651 | 0.9160 | 0.8898 | 0.9798 | 0.9679 | 0.9738 |
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- | 0.0297 | 9.0 | 1539 | 0.5912 | 0.9483 | 0.9498 | 0.6649 | 0.7622 | 0.7102 | 0.8615 | 0.9412 | 0.8996 | 0.9802 | 0.9636 | 0.9718 |
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- | 0.0297 | 10.0 | 1710 | 0.6120 | 0.9496 | 0.9509 | 0.6757 | 0.7622 | 0.7163 | 0.8672 | 0.9328 | 0.8988 | 0.9798 | 0.9655 | 0.9726 |
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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 2.19.1
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  - Tokenizers 0.19.1
 
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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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  - 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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