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distilgpt2-finetuned

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End of training

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  1. README.md +9 -9
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -17,11 +17,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 3.8114
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- - Bleu: 0.0101
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- - Bertscore Precision: 0.1499
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- - Bertscore Recall: 0.1656
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- - Bertscore F1: 0.1571
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  ## Model description
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@@ -44,18 +44,18 @@ The following hyperparameters were used during training:
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  - train_batch_size: 8
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  - eval_batch_size: 8
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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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- - num_epochs: 3.0
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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 | Bleu | Bertscore Precision | Bertscore Recall | Bertscore F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------:|:----------------:|:------------:|
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- | 4.1924 | 1.0 | 644 | 4.0681 | 0.0091 | 0.1493 | 0.1649 | 0.1564 |
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- | 4.0754 | 2.0 | 1288 | 3.8779 | 0.0099 | 0.1498 | 0.1654 | 0.1569 |
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- | 3.8277 | 3.0 | 1932 | 3.8114 | 0.0101 | 0.1499 | 0.1656 | 0.1571 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 4.0665
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+ - Bleu: 0.0085
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+ - Bertscore Precision: 0.1478
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+ - Bertscore Recall: 0.1636
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+ - Bertscore F1: 0.1550
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  ## Model description
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 16
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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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+ - num_epochs: 1
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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 | Bleu | Bertscore Precision | Bertscore Recall | Bertscore F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------:|:----------------:|:------------:|
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+ | 5.0162 | 1.0 | 3223 | 4.0665 | 0.0085 | 0.1478 | 0.1636 | 0.1550 |
 
 
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  ### Framework versions
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