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COPA_E_CARE_XLM_roberta_base_finetuned

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  1. README.md +13 -10
  2. model.safetensors +1 -1
  3. runs/Jun23_02-59-26_ebd7d1350a7d/events.out.tfevents.1719111567.ebd7d1350a7d.1507.1 +2 -2
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Ariffiq99/e_care_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/e_care_xlm_roberta_base_finetuned) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8920
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- - F1: 0.666
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  ## Model description
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@@ -43,19 +43,22 @@ The following hyperparameters were used during training:
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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: 7
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:-----:|
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- | No log | 1.0 | 250 | 0.6792 | 0.618 |
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- | 0.6869 | 2.0 | 500 | 0.6399 | 0.672 |
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- | 0.6869 | 3.0 | 750 | 0.6307 | 0.664 |
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- | 0.5777 | 4.0 | 1000 | 0.7197 | 0.684 |
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- | 0.5777 | 5.0 | 1250 | 0.7893 | 0.668 |
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- | 0.4885 | 6.0 | 1500 | 0.8782 | 0.638 |
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- | 0.4885 | 7.0 | 1750 | 0.8920 | 0.666 |
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [Ariffiq99/e_care_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/e_care_xlm_roberta_base_finetuned) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.7353
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+ - F1: 0.67
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  ## Model description
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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: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:-----:|
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+ | No log | 1.0 | 250 | 0.9393 | 0.668 |
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+ | 0.2405 | 2.0 | 500 | 1.1072 | 0.684 |
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+ | 0.2405 | 3.0 | 750 | 1.1488 | 0.648 |
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+ | 0.3938 | 4.0 | 1000 | 0.9665 | 0.652 |
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+ | 0.3938 | 5.0 | 1250 | 1.2431 | 0.654 |
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+ | 0.4987 | 6.0 | 1500 | 1.2893 | 0.674 |
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+ | 0.4987 | 7.0 | 1750 | 1.5097 | 0.68 |
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+ | 0.3532 | 8.0 | 2000 | 1.6731 | 0.664 |
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+ | 0.3532 | 9.0 | 2250 | 1.7344 | 0.664 |
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+ | 0.3011 | 10.0 | 2500 | 1.7353 | 0.67 |
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  ### Framework versions
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