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lora_fine_tuned_copa_XLMroberta

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Files changed (7) hide show
  1. README.md +12 -12
  2. adapter_model.safetensors +1 -1
  3. runs/May21_19-49-20_DESKTOP-22QTFDR/events.out.tfevents.1716313765.DESKTOP-22QTFDR.33008.2 +3 -0
  4. runs/May21_19-49-20_DESKTOP-22QTFDR/events.out.tfevents.1716313787.DESKTOP-22QTFDR.33008.3 +3 -0
  5. special_tokens_map.json +42 -6
  6. tokenizer.json +2 -2
  7. training_args.bin +1 -1
README.md CHANGED
@@ -19,9 +19,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6931
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- - Accuracy: 0.52
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- - F1: 0.5067
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  ## Model description
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@@ -40,7 +40,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.003
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -52,14 +52,14 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 0.7167 | 1.0 | 50 | 0.6931 | 0.45 | 0.4514 |
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- | 0.6965 | 2.0 | 100 | 0.6931 | 0.45 | 0.4514 |
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- | 0.7021 | 3.0 | 150 | 0.6931 | 0.53 | 0.5310 |
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- | 0.701 | 4.0 | 200 | 0.6931 | 0.46 | 0.4613 |
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- | 0.7091 | 5.0 | 250 | 0.6931 | 0.51 | 0.5104 |
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- | 0.6972 | 6.0 | 300 | 0.6931 | 0.5 | 0.4896 |
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- | 0.6983 | 7.0 | 350 | 0.6931 | 0.56 | 0.56 |
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- | 0.6961 | 8.0 | 400 | 0.6931 | 0.52 | 0.5067 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6929
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+ - Accuracy: 0.59
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+ - F1: 0.5884
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  ## Model description
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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: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 0.6985 | 1.0 | 50 | 0.6928 | 0.57 | 0.5695 |
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+ | 0.7012 | 2.0 | 100 | 0.6928 | 0.58 | 0.58 |
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+ | 0.6935 | 3.0 | 150 | 0.6929 | 0.58 | 0.5790 |
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+ | 0.6897 | 4.0 | 200 | 0.6929 | 0.58 | 0.5776 |
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+ | 0.6973 | 5.0 | 250 | 0.6929 | 0.6 | 0.5990 |
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+ | 0.6982 | 6.0 | 300 | 0.6929 | 0.59 | 0.5884 |
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+ | 0.6981 | 7.0 | 350 | 0.6929 | 0.59 | 0.5884 |
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+ | 0.6999 | 8.0 | 400 | 0.6929 | 0.59 | 0.5884 |
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  ### Framework versions
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