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distilbert-base-uncased-finetuned-ner

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update model card README.md

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@@ -22,16 +22,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9262785034314811
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  - name: Recall
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  type: recall
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- value: 0.9361226087929299
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  - name: F1
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  type: f1
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- value: 0.9311745395871586
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  - name: Accuracy
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  type: accuracy
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- value: 0.9836529143565221
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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
@@ -41,11 +41,11 @@ 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: 0.0625
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- - Precision: 0.9263
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- - Recall: 0.9361
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- - F1: 0.9312
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- - Accuracy: 0.9837
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  ## Model description
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@@ -76,14 +76,14 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.2383 | 1.0 | 878 | 0.0718 | 0.9098 | 0.9205 | 0.9151 | 0.9806 |
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- | 0.0524 | 2.0 | 1756 | 0.0631 | 0.9197 | 0.9318 | 0.9257 | 0.9828 |
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- | 0.0318 | 3.0 | 2634 | 0.0625 | 0.9263 | 0.9361 | 0.9312 | 0.9837 |
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  ### Framework versions
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- - Transformers 4.12.5
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  - Pytorch 1.10.0+cu111
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- - Datasets 1.16.1
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  - Tokenizers 0.10.3
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9266592920353982
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  - name: Recall
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  type: recall
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+ value: 0.9371294328224634
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  - name: F1
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  type: f1
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+ value: 0.9318649535569274
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9838117781625813
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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: 0.0620
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+ - Precision: 0.9267
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+ - Recall: 0.9371
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+ - F1: 0.9319
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+ - Accuracy: 0.9838
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.2462 | 1.0 | 878 | 0.0714 | 0.9052 | 0.9223 | 0.9137 | 0.9803 |
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+ | 0.0535 | 2.0 | 1756 | 0.0615 | 0.9188 | 0.9331 | 0.9259 | 0.9827 |
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+ | 0.0315 | 3.0 | 2634 | 0.0620 | 0.9267 | 0.9371 | 0.9319 | 0.9838 |
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  ### Framework versions
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+ - Transformers 4.15.0
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  - Pytorch 1.10.0+cu111
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+ - Datasets 1.17.0
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  - Tokenizers 0.10.3