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Dagobert42
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distilbert-base-uncased-biored-augmented

Token Classification
Transformers
Safetensors
English
distilbert
low-resource NER
token_classification
biomedicine
medical NER
Generated from Trainer
Inference Endpoints
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Dagobert42 commited on
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Push ../models/distilbert-base-uncased/biored-augmentations-only/ trained on biored-train_200_splits.pt (200 samples)

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Files changed (3) hide show
  1. README.md +11 -12
  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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the bigbio/biored dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5931
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- - Accuracy: 0.794
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- - Precision: 0.6117
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- - Recall: 0.4962
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- - F1: 0.5339
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- - Weighted F1: 0.7824
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  ## Model description
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@@ -64,12 +64,11 @@ 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 | 13 | 0.6199 | 0.7791 | 0.6445 | 0.4144 | 0.4762 | 0.7573 |
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- | No log | 2.0 | 26 | 0.6068 | 0.7865 | 0.6465 | 0.4497 | 0.5064 | 0.7621 |
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- | No log | 3.0 | 39 | 0.5978 | 0.7879 | 0.6267 | 0.4835 | 0.5232 | 0.7757 |
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- | No log | 4.0 | 52 | 0.5912 | 0.7942 | 0.6342 | 0.5039 | 0.546 | 0.7801 |
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- | No log | 5.0 | 65 | 0.5973 | 0.7961 | 0.6716 | 0.4927 | 0.5403 | 0.7797 |
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- | No log | 6.0 | 78 | 0.5925 | 0.7938 | 0.6192 | 0.5399 | 0.5572 | 0.7913 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the bigbio/biored dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5843
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+ - Accuracy: 0.7932
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+ - Precision: 0.599
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+ - Recall: 0.5407
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+ - F1: 0.5617
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+ - Weighted F1: 0.7888
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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 | 25 | 0.6232 | 0.7797 | 0.5883 | 0.4544 | 0.5018 | 0.7583 |
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+ | No log | 2.0 | 50 | 0.6072 | 0.7871 | 0.575 | 0.4951 | 0.5268 | 0.7726 |
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+ | No log | 3.0 | 75 | 0.6059 | 0.7869 | 0.5665 | 0.5208 | 0.5389 | 0.7788 |
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+ | No log | 4.0 | 100 | 0.6088 | 0.791 | 0.5756 | 0.5006 | 0.5282 | 0.7742 |
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+ | No log | 5.0 | 125 | 0.6066 | 0.7916 | 0.5761 | 0.5067 | 0.5361 | 0.7781 |
 
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  ### Framework versions
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