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LogischeIP
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SentimentT2_GPT2

Text Classification
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gpt2
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End of training

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  1. README.md +12 -8
  2. model.safetensors +1 -1
  3. runs/Jan24_20-13-09_7d2b1e2d4e1a/events.out.tfevents.1706127190.7d2b1e2d4e1a.26662.10 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -18,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7249
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- - Accuracy: 0.8669
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- - F1: 0.8749
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- - Auc Roc: 0.9317
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- - Log Loss: 0.7249
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  ## Model description
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@@ -50,15 +50,19 @@ 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: cosine
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  - lr_scheduler_warmup_steps: 500
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- - num_epochs: 2
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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 | Auc Roc | Log Loss |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:--------:|
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- | 1.1736 | 1.0 | 1618 | 0.6146 | 0.8507 | 0.8592 | 0.9256 | 0.6146 |
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- | 0.6452 | 2.0 | 3236 | 0.7249 | 0.8669 | 0.8749 | 0.9317 | 0.7249 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9470
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+ - Accuracy: 0.8644
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+ - F1: 0.8728
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+ - Auc Roc: 0.9185
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+ - Log Loss: 0.9470
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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: cosine
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  - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 6
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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 | Auc Roc | Log Loss |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:--------:|
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+ | 1.0534 | 1.0 | 1618 | 0.6479 | 0.8694 | 0.8696 | 0.9298 | 0.6479 |
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+ | 0.6971 | 2.0 | 3236 | 1.0859 | 0.8371 | 0.8581 | 0.9236 | 1.0859 |
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+ | 0.5832 | 3.0 | 4854 | 0.9261 | 0.8495 | 0.8672 | 0.9255 | 0.9261 |
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+ | 0.4402 | 4.0 | 6472 | 0.8507 | 0.8719 | 0.8804 | 0.9251 | 0.8507 |
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+ | 0.3475 | 5.0 | 8090 | 0.9284 | 0.8657 | 0.8735 | 0.9198 | 0.9283 |
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+ | 0.2985 | 6.0 | 9708 | 0.9470 | 0.8644 | 0.8728 | 0.9185 | 0.9470 |
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  ### Framework versions
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