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xlnet-base-cased-biored-augmented

Token Classification
Transformers
Safetensors
English
xlnet
low-resource NER
token_classification
biomedicine
medical NER
Generated from Trainer
Inference Endpoints
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Dagobert42 commited on
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1 Parent(s): 7c3a770

Push ../models/xlnet/xlnet-base-cased/biored-augmentations-only/ trained on biored-train_160_splits.pt (160 samples)

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Files changed (3) hide show
  1. README.md +17 -13
  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: 0.1502
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- - Accuracy: 0.9559
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- - Precision: 0.8713
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- - Recall: 0.8616
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- - F1: 0.8662
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- - Weighted F1: 0.9557
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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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 | 20 | 0.2115 | 0.9292 | 0.7915 | 0.7951 | 0.7898 | 0.9295 |
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- | No log | 2.0 | 40 | 0.1922 | 0.9393 | 0.8186 | 0.8046 | 0.8081 | 0.9386 |
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- | No log | 3.0 | 60 | 0.1912 | 0.944 | 0.845 | 0.8163 | 0.83 | 0.943 |
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- | No log | 4.0 | 80 | 0.1890 | 0.9462 | 0.8396 | 0.829 | 0.833 | 0.9454 |
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- | No log | 5.0 | 100 | 0.2025 | 0.9456 | 0.8273 | 0.8257 | 0.825 | 0.945 |
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- | No log | 6.0 | 120 | 0.2126 | 0.946 | 0.846 | 0.8276 | 0.8352 | 0.9454 |
 
 
 
 
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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: 0.1676
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+ - Accuracy: 0.9551
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+ - Precision: 0.8878
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+ - Recall: 0.847
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+ - F1: 0.8624
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+ - Weighted F1: 0.9551
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1.8e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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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 | 20 | 0.2094 | 0.9301 | 0.7924 | 0.7914 | 0.7886 | 0.9301 |
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+ | No log | 2.0 | 40 | 0.1915 | 0.9391 | 0.8202 | 0.8032 | 0.808 | 0.9383 |
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+ | No log | 3.0 | 60 | 0.1901 | 0.9425 | 0.8239 | 0.8169 | 0.8197 | 0.9418 |
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+ | No log | 4.0 | 80 | 0.1872 | 0.9461 | 0.8361 | 0.8277 | 0.8304 | 0.9453 |
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+ | No log | 5.0 | 100 | 0.2001 | 0.9455 | 0.8269 | 0.8251 | 0.8245 | 0.9448 |
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+ | No log | 6.0 | 120 | 0.2063 | 0.9462 | 0.845 | 0.8288 | 0.8354 | 0.9457 |
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+ | No log | 7.0 | 140 | 0.2081 | 0.9458 | 0.8153 | 0.8353 | 0.8235 | 0.9458 |
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+ | No log | 8.0 | 160 | 0.2274 | 0.9454 | 0.8192 | 0.8329 | 0.8245 | 0.9452 |
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+ | No log | 9.0 | 180 | 0.2286 | 0.9475 | 0.8298 | 0.8332 | 0.8303 | 0.9471 |
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+ | No log | 10.0 | 200 | 0.2404 | 0.9473 | 0.8352 | 0.83 | 0.8314 | 0.9467 |
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  ### Framework versions
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