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xlnet-base-cased-biored-finetuned

Token Classification
Transformers
Safetensors
English
xlnet
low-resource NER
token_classification
biomedicine
medical NER
Generated from Trainer
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Push xlnet/xlnet-base-cased trained on biored-original_splits.pt (100 samples)

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  1. README.md +13 -14
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -28,12 +28,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7341
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- - Accuracy: 0.7714
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- - Precision: 0.5341
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- - Recall: 0.4169
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- - F1: 0.4594
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- - Weighted F1: 0.7495
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  ## Model description
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@@ -64,14 +64,13 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
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- | No log | 1.0 | 25 | 0.8831 | 0.7377 | 0.4536 | 0.2481 | 0.2822 | 0.6719 |
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- | No log | 2.0 | 50 | 0.8309 | 0.7542 | 0.6035 | 0.3177 | 0.3598 | 0.6933 |
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- | No log | 3.0 | 75 | 0.7695 | 0.7624 | 0.568 | 0.3566 | 0.409 | 0.7189 |
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- | No log | 4.0 | 100 | 0.7562 | 0.7676 | 0.5536 | 0.3886 | 0.4398 | 0.7343 |
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- | No log | 5.0 | 125 | 0.7540 | 0.7673 | 0.5711 | 0.4013 | 0.4474 | 0.7368 |
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- | No log | 6.0 | 150 | 0.7425 | 0.7754 | 0.5867 | 0.4398 | 0.4873 | 0.7514 |
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- | No log | 7.0 | 175 | 0.7806 | 0.7788 | 0.606 | 0.4235 | 0.473 | 0.7475 |
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- | No log | 8.0 | 200 | 0.7638 | 0.7785 | 0.558 | 0.4547 | 0.4871 | 0.7549 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7909
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+ - Accuracy: 0.756
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+ - Precision: 0.5226
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+ - Recall: 0.3518
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+ - F1: 0.4027
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+ - Weighted F1: 0.7173
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
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+ | No log | 1.0 | 13 | 0.9045 | 0.7251 | 0.3378 | 0.1564 | 0.1457 | 0.6233 |
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+ | No log | 2.0 | 26 | 0.8688 | 0.7336 | 0.5559 | 0.2281 | 0.2504 | 0.6453 |
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+ | No log | 3.0 | 39 | 0.8579 | 0.7409 | 0.5851 | 0.2931 | 0.3179 | 0.6795 |
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+ | No log | 4.0 | 52 | 0.7956 | 0.7507 | 0.5225 | 0.3443 | 0.3919 | 0.7017 |
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+ | No log | 5.0 | 65 | 0.7947 | 0.7529 | 0.532 | 0.3535 | 0.4026 | 0.7093 |
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+ | No log | 6.0 | 78 | 0.8063 | 0.7549 | 0.5502 | 0.3752 | 0.4191 | 0.7168 |
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+ | No log | 7.0 | 91 | 0.8059 | 0.7599 | 0.5496 | 0.3764 | 0.4269 | 0.7227 |
 
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  ### Framework versions
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