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mirfan899
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hindi-muril-ner

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +11 -12
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0050
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- - Precision: 0.9870
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- - Recall: 0.9892
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- - F1: 0.9881
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- - Accuracy: 0.9989
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  ## Model description
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@@ -43,21 +43,20 @@ 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: 0.0001
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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: 3
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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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- | 0.1968 | 1.0 | 882 | 0.0436 | 0.8199 | 0.8751 | 0.8466 | 0.9884 |
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- | 0.0212 | 2.0 | 1764 | 0.0106 | 0.9695 | 0.9704 | 0.9700 | 0.9975 |
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- | 0.0038 | 3.0 | 2646 | 0.0050 | 0.9870 | 0.9892 | 0.9881 | 0.9989 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5652
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+ - Precision: 0.0
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+ - Recall: 0.0
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+ - F1: 0.0
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+ - Accuracy: 0.9029
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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: 0.001
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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: 2
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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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+ | 0.6329 | 1.0 | 882 | 0.5652 | 0.0 | 0.0 | 0.0 | 0.9029 |
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+ | 0.586 | 2.0 | 1764 | 0.5652 | 0.0 | 0.0 | 0.0 | 0.9029 |
 
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  ### Framework versions
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