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End of training

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  1. README.md +19 -4
  2. model.safetensors +1 -1
README.md CHANGED
@@ -1,9 +1,14 @@
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  ---
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  library_name: transformers
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  license: apache-2.0
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- base_model: distilbert/distilbert-base-uncased
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  tags:
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  - generated_from_trainer
 
 
 
 
 
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  model-index:
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  - name: token_classification_NER
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  results: []
@@ -14,7 +19,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # token_classification_NER
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- This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
 
 
 
 
 
 
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  ## Model description
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@@ -39,13 +50,17 @@ The following hyperparameters were used during training:
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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: 1
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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 | 213 | 0.3104 | 0.3900 | 0.0871 | 0.1424 | 0.9316 |
 
 
 
 
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  ### Framework versions
 
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  ---
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  library_name: transformers
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  license: apache-2.0
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+ base_model: dany0407/token_classification_NER
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  tags:
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  - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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  model-index:
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  - name: token_classification_NER
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  results: []
 
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  # token_classification_NER
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+ This model is a fine-tuned version of [dany0407/token_classification_NER](https://huggingface.co/dany0407/token_classification_NER) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3086
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+ - Precision: 0.5215
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+ - Recall: 0.3939
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+ - F1: 0.4488
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+ - Accuracy: 0.9461
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  ## Model description
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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: 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 | 213 | 0.2594 | 0.6151 | 0.3095 | 0.4118 | 0.9428 |
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+ | No log | 2.0 | 426 | 0.2813 | 0.5541 | 0.3466 | 0.4265 | 0.9444 |
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+ | 0.0965 | 3.0 | 639 | 0.3013 | 0.5584 | 0.3587 | 0.4368 | 0.9455 |
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+ | 0.0965 | 4.0 | 852 | 0.3036 | 0.5416 | 0.3865 | 0.4511 | 0.9463 |
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+ | 0.0375 | 5.0 | 1065 | 0.3086 | 0.5215 | 0.3939 | 0.4488 | 0.9461 |
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  ### Framework versions
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@@ -1,3 +1,3 @@
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