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distilbert-base-uncased-finetuned-ner

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update model card README.md

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9164185687506892
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  - name: Recall
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  type: recall
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- value: 0.9297460566058843
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  - name: F1
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  type: f1
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- value: 0.9230342070191025
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  - name: Accuracy
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  type: accuracy
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- value: 0.9818895261092665
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 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: 0.9164
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- - Recall: 0.9297
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- - F1: 0.9230
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- - Accuracy: 0.9819
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  ## Model description
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@@ -72,20 +72,22 @@ The following hyperparameters were used during training:
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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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- | No log | 1.0 | 439 | 0.0792 | 0.8879 | 0.9023 | 0.8951 | 0.9774 |
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- | 0.2186 | 2.0 | 878 | 0.0635 | 0.9119 | 0.9278 | 0.9198 | 0.9817 |
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- | 0.0594 | 3.0 | 1317 | 0.0638 | 0.9164 | 0.9297 | 0.9230 | 0.9819 |
 
 
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  ### Framework versions
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  - Transformers 4.28.1
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  - Pytorch 2.0.0+cu118
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- - Datasets 2.11.0
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  - Tokenizers 0.13.3
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9264624571491762
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  - name: Recall
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  type: recall
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+ value: 0.9372413021590782
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  - name: F1
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  type: f1
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+ value: 0.9318207095984874
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9840024147298521
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0621
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+ - Precision: 0.9265
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+ - Recall: 0.9372
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+ - F1: 0.9318
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+ - Accuracy: 0.9840
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  ## Model description
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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 | 439 | 0.0751 | 0.8976 | 0.9103 | 0.9039 | 0.9789 |
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+ | 0.219 | 2.0 | 878 | 0.0626 | 0.9130 | 0.9284 | 0.9206 | 0.9817 |
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+ | 0.0558 | 3.0 | 1317 | 0.0623 | 0.9195 | 0.9332 | 0.9263 | 0.9826 |
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+ | 0.0321 | 4.0 | 1756 | 0.0610 | 0.9251 | 0.9359 | 0.9305 | 0.9835 |
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+ | 0.0228 | 5.0 | 2195 | 0.0621 | 0.9265 | 0.9372 | 0.9318 | 0.9840 |
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  ### Framework versions
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  - Transformers 4.28.1
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  - Pytorch 2.0.0+cu118
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+ - Datasets 2.12.0
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  - Tokenizers 0.13.3