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xlm-roberta-base-finetuned-panx-de

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  1. README.md +7 -7
  2. model.safetensors +1 -1
  3. runs/Nov16_14-45-41_Kang-PC/events.out.tfevents.1731760592.Kang-PC.32612.1 +3 -0
  4. runs/Nov16_14-45-41_Kang-PC/events.out.tfevents.1731761129.Kang-PC.32612.2 +3 -0
  5. runs/Nov16_14-45-41_Kang-PC/events.out.tfevents.1731761775.Kang-PC.32612.3 +3 -0
  6. runs/Nov16_14-45-41_Kang-PC/events.out.tfevents.1731762681.Kang-PC.32612.4 +3 -0
  7. runs/Nov16_14-45-41_Kang-PC/events.out.tfevents.1731764138.Kang-PC.32612.5 +3 -0
  8. runs/Nov16_14-45-41_Kang-PC/events.out.tfevents.1731818184.Kang-PC.32612.6 +3 -0
  9. runs/Nov23_17-57-24_Kang-PC/events.out.tfevents.1732352253.Kang-PC.31280.0 +3 -0
  10. training_args.bin +1 -1
README.md CHANGED
@@ -18,8 +18,8 @@ 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: 0.1370
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- - F1: 0.8613
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  ## Model description
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@@ -42,7 +42,7 @@ The following hyperparameters were used during training:
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  - train_batch_size: 24
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  - eval_batch_size: 24
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  - seed: 42
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- - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - num_epochs: 3
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@@ -50,14 +50,14 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 0.2593 | 1.0 | 525 | 0.1490 | 0.8217 |
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- | 0.1251 | 2.0 | 1050 | 0.1366 | 0.8511 |
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- | 0.0802 | 3.0 | 1575 | 0.1370 | 0.8613 |
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  ### Framework versions
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  - Transformers 4.46.2
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- - Pytorch 2.4.1+cu118
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  - Datasets 3.1.0
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  - Tokenizers 0.20.3
 
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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: 0.1376
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+ - F1: 0.8644
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  ## Model description
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  - train_batch_size: 24
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  - eval_batch_size: 24
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  - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - num_epochs: 3
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.2579 | 1.0 | 525 | 0.1546 | 0.8179 |
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+ | 0.1283 | 2.0 | 1050 | 0.1378 | 0.8518 |
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+ | 0.0805 | 3.0 | 1575 | 0.1376 | 0.8644 |
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  ### Framework versions
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  - Transformers 4.46.2
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+ - Pytorch 2.5.1+cpu
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  - Datasets 3.1.0
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  - Tokenizers 0.20.3
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