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Jise
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bert-finetuned-ner

Token Classification
Transformers
Safetensors
gpt2
Generated from Trainer
Eval Results
text-generation-inference
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Jise commited on
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Training complete

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  1. README.md +12 -12
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.09849521203830369
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  - name: Recall
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  type: recall
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- value: 0.09148665819567979
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  - name: F1
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  type: f1
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- value: 0.09486166007905138
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  - name: Accuracy
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  type: accuracy
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- value: 0.9378816756777782
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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
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2142
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- - Precision: 0.0985
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- - Recall: 0.0915
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- - F1: 0.0949
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- - Accuracy: 0.9379
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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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- | 0.3511 | 1.0 | 679 | 0.2305 | 0.0201 | 0.0140 | 0.0165 | 0.9314 |
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- | 0.2452 | 2.0 | 1358 | 0.2158 | 0.0630 | 0.0584 | 0.0606 | 0.9357 |
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- | 0.1809 | 3.0 | 2037 | 0.2142 | 0.0985 | 0.0915 | 0.0949 | 0.9379 |
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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: 0.08217270194986072
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  - name: Recall
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  type: recall
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+ value: 0.07496823379923762
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  - name: F1
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  type: f1
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+ value: 0.07840531561461794
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9369870473375083
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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 [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2151
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+ - Precision: 0.0822
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+ - Recall: 0.0750
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+ - F1: 0.0784
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+ - Accuracy: 0.9370
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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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+ | 0.3388 | 1.0 | 679 | 0.2280 | 0.0292 | 0.0254 | 0.0272 | 0.9312 |
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+ | 0.2425 | 2.0 | 1358 | 0.2161 | 0.0612 | 0.0572 | 0.0591 | 0.9345 |
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+ | 0.1811 | 3.0 | 2037 | 0.2151 | 0.0822 | 0.0750 | 0.0784 | 0.9370 |
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  ### Framework versions
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