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MedTXTNER

Token Classification
Transformers
Safetensors
bert
japanese
ner
medical
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xet
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@@ -71,30 +71,9 @@ for tok, lab in predict_text(sample):
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  ## Evaluation
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- ### Overall Performance
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- | Metric | Score |
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- |-----------------|-------:|
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- | Micro‑F1 | 0.638 |
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- | Macro‑F1 | 0.480 |
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- | Weighted‑F1 | 0.641 |
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-
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- ### MedTxt-CR Performance
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- | Metric | Score |
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- |-----------------|-------:|
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- | Micro‑F1 | 0.551 |
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- | Macro‑F1 | 0.396 |
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- | Weighted‑F1 | 0.559 |
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-
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- ### MedTxt-RR Performance
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- | Metric | Score |
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- |-----------------|-------:|
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- | Micro‑F1 | 0.887 |
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- | Macro‑F1 | 0.708 |
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- | Weighted‑F1 | 0.888 |
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-
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- ### MedTxt-NR Performance
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- | Metric | Score |
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- |-----------------|-------:|
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- | Micro‑F1 | 0.730 |
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- | Macro‑F1 | 0.552 |
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- | Weighted‑F1 | 0.731 |
 
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  ## Evaluation
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+ | Dataset | Micro‑F1 | Macro‑F1 | Weighted‑F1 |
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+ | -------------- | --------:| --------:| -----------:|
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+ | **Overall** | 0.638 | 0.480 | 0.641 |
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+ | **MedTxt‑CR** | 0.551 | 0.396 | 0.559 |
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+ | **MedTxt‑RR** | 0.887 | 0.708 | 0.888 |
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+ | **MedTxt‑NR** | 0.730 | 0.552 | 0.731 |