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vonewman
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wolof-finetuned-ner

Token Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Generated from Trainer
Inference Endpoints
Model card Files Community
1
vonewman commited on
Commit
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1 Parent(s): 1ecbdfd

Training complete

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  1. README.md +12 -11
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -18,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0638
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- - Precision: 0.8534
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- - Recall: 0.8912
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- - F1: 0.8719
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- - Accuracy: 0.9886
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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: 3.6925210602772034e-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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  - 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: 4
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 226 | 0.0697 | 0.7640 | 0.8810 | 0.8183 | 0.9849 |
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- | No log | 2.0 | 452 | 0.0663 | 0.8522 | 0.8435 | 0.8479 | 0.9876 |
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- | 0.0089 | 3.0 | 678 | 0.0689 | 0.8257 | 0.9184 | 0.8696 | 0.9875 |
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- | 0.0089 | 4.0 | 904 | 0.0638 | 0.8534 | 0.8912 | 0.8719 | 0.9886 |
 
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  ### Framework versions
 
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  This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3572
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+ - Precision: 0.7719
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+ - Recall: 0.8980
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+ - F1: 0.8302
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+ - Accuracy: 0.9845
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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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  - 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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 226 | 0.3649 | 0.7690 | 0.8605 | 0.8122 | 0.9852 |
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+ | No log | 2.0 | 452 | 0.3350 | 0.8088 | 0.8776 | 0.8418 | 0.9861 |
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+ | 0.4546 | 3.0 | 678 | 0.3737 | 0.7842 | 0.8776 | 0.8283 | 0.9848 |
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+ | 0.4546 | 4.0 | 904 | 0.3271 | 0.7713 | 0.8946 | 0.8283 | 0.9841 |
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+ | 0.0992 | 5.0 | 1130 | 0.3572 | 0.7719 | 0.8980 | 0.8302 | 0.9845 |
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  ### Framework versions
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