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RKessler
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VogagenRelation

Text Classification
Transformers
PyTorch
camembert
Generated from Trainer
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End of training

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  1. README.md +44 -8
  2. pytorch_model.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 [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4272
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- - Accuracy: 0.8339
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  ## Model description
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@@ -37,20 +37,56 @@ 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: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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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: 2
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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 | 1.0 | 241 | 0.4897 | 0.7995 |
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- | No log | 2.0 | 482 | 0.4272 | 0.8339 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6932
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+ - Accuracy: 0.5059
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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: 1e-08
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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: 8
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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 | 0.21 | 100 | 0.6932 | 0.5043 |
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+ | No log | 0.42 | 200 | 0.6932 | 0.5051 |
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+ | No log | 0.62 | 300 | 0.6932 | 0.5051 |
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+ | No log | 0.83 | 400 | 0.6932 | 0.5051 |
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+ | 0.693 | 1.04 | 500 | 0.6932 | 0.5051 |
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+ | 0.693 | 1.25 | 600 | 0.6932 | 0.5051 |
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+ | 0.693 | 1.46 | 700 | 0.6932 | 0.5043 |
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+ | 0.693 | 1.66 | 800 | 0.6932 | 0.5043 |
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+ | 0.693 | 1.87 | 900 | 0.6932 | 0.5051 |
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+ | 0.693 | 2.08 | 1000 | 0.6932 | 0.5043 |
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+ | 0.693 | 2.29 | 1100 | 0.6932 | 0.5043 |
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+ | 0.693 | 2.49 | 1200 | 0.6932 | 0.5043 |
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+ | 0.693 | 2.7 | 1300 | 0.6932 | 0.5043 |
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+ | 0.693 | 2.91 | 1400 | 0.6932 | 0.5043 |
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+ | 0.6938 | 3.12 | 1500 | 0.6932 | 0.5043 |
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+ | 0.6938 | 3.33 | 1600 | 0.6932 | 0.5043 |
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+ | 0.6938 | 3.53 | 1700 | 0.6932 | 0.5043 |
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+ | 0.6938 | 3.74 | 1800 | 0.6932 | 0.5043 |
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+ | 0.6938 | 3.95 | 1900 | 0.6932 | 0.5043 |
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+ | 0.693 | 4.16 | 2000 | 0.6932 | 0.5051 |
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+ | 0.693 | 4.37 | 2100 | 0.6932 | 0.5051 |
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+ | 0.693 | 4.57 | 2200 | 0.6932 | 0.5051 |
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+ | 0.693 | 4.78 | 2300 | 0.6932 | 0.5051 |
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+ | 0.693 | 4.99 | 2400 | 0.6932 | 0.5051 |
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+ | 0.6927 | 5.2 | 2500 | 0.6932 | 0.5051 |
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+ | 0.6927 | 5.41 | 2600 | 0.6932 | 0.5051 |
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+ | 0.6927 | 5.61 | 2700 | 0.6932 | 0.5051 |
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+ | 0.6927 | 5.82 | 2800 | 0.6932 | 0.5051 |
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+ | 0.6927 | 6.03 | 2900 | 0.6932 | 0.5051 |
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+ | 0.6927 | 6.24 | 3000 | 0.6932 | 0.5051 |
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+ | 0.6927 | 6.44 | 3100 | 0.6932 | 0.5059 |
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+ | 0.6927 | 6.65 | 3200 | 0.6932 | 0.5059 |
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+ | 0.6927 | 6.86 | 3300 | 0.6932 | 0.5059 |
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+ | 0.6927 | 7.07 | 3400 | 0.6932 | 0.5059 |
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+ | 0.6934 | 7.28 | 3500 | 0.6932 | 0.5059 |
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+ | 0.6934 | 7.48 | 3600 | 0.6932 | 0.5059 |
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+ | 0.6934 | 7.69 | 3700 | 0.6932 | 0.5059 |
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+ | 0.6934 | 7.9 | 3800 | 0.6932 | 0.5059 |
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  ### Framework versions
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