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distilbert-base-uncased-finetuned-ner

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distilbert
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End of training

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  1. README.md +11 -11
  2. model.safetensors +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9281357435954308
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  - name: Recall
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  type: recall
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- value: 0.9362344781295447
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  - name: F1
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  type: f1
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- value: 0.9321675206059256
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  - name: Accuracy
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  type: accuracy
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- value: 0.983668800737128
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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
@@ -44,11 +44,11 @@ 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: 0.0599
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- - Precision: 0.9281
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- - Recall: 0.9362
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  - F1: 0.9322
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- - Accuracy: 0.9837
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  ## Model description
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@@ -79,9 +79,9 @@ 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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- | 0.2462 | 1.0 | 878 | 0.0703 | 0.8956 | 0.9127 | 0.9041 | 0.9789 |
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- | 0.0511 | 2.0 | 1756 | 0.0597 | 0.9241 | 0.9320 | 0.9280 | 0.9828 |
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- | 0.0311 | 3.0 | 2634 | 0.0599 | 0.9281 | 0.9362 | 0.9322 | 0.9837 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9273773939997786
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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: 0.9322279100823503
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9834146186474335
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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: 0.0618
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+ - Precision: 0.9274
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+ - Recall: 0.9371
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  - F1: 0.9322
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+ - Accuracy: 0.9834
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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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+ | 0.253 | 1.0 | 878 | 0.0708 | 0.9027 | 0.9177 | 0.9101 | 0.9795 |
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+ | 0.0518 | 2.0 | 1756 | 0.0624 | 0.9204 | 0.9329 | 0.9266 | 0.9825 |
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+ | 0.031 | 3.0 | 2634 | 0.0618 | 0.9274 | 0.9371 | 0.9322 | 0.9834 |
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  ### Framework versions
model.safetensors CHANGED
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