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adriansanz
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modelofine2

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +14 -4
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
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 [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.5528
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- - Accuracy: 0.5798
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  ## Model description
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@@ -44,14 +44,24 @@ 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: linear
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  - lr_scheduler_warmup_steps: 500
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- - num_epochs: 30
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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 |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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- | 1.1268 | 16.1290 | 500 | 2.0767 | 0.5210 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.6811
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+ - Accuracy: 0.6471
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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: linear
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  - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 60
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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 |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | No log | 3.2258 | 100 | 2.7256 | 0.5546 |
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+ | No log | 6.4516 | 200 | 2.7585 | 0.5714 |
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+ | No log | 9.6774 | 300 | 2.9830 | 0.5378 |
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+ | No log | 12.9032 | 400 | 3.8506 | 0.5042 |
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+ | 0.0468 | 16.1290 | 500 | 3.4211 | 0.5294 |
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+ | 0.0468 | 19.3548 | 600 | 2.7938 | 0.5882 |
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+ | 0.0468 | 22.5806 | 700 | 2.9805 | 0.6050 |
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+ | 0.0468 | 25.8065 | 800 | 2.9111 | 0.5882 |
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+ | 0.0468 | 29.0323 | 900 | 2.7054 | 0.6134 |
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+ | 0.0358 | 32.2581 | 1000 | 2.6811 | 0.6471 |
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+ | 0.0358 | 35.4839 | 1100 | 2.8372 | 0.6134 |
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  ### Framework versions
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