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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-original_splits.pt (200 samples)

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Files changed (3) hide show
  1. README.md +10 -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: 0.5260
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- - Accuracy: 0.8133
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- - Precision: 0.6083
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- - Recall: 0.5678
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- - F1: 0.5839
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- - Weighted F1: 0.8098
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  ## Model description
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@@ -64,12 +64,10 @@ 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 | 0.5511 | 0.8048 | 0.594 | 0.5104 | 0.5398 | 0.792 |
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- | No log | 2.0 | 50 | 0.5480 | 0.8075 | 0.6351 | 0.5573 | 0.5824 | 0.8 |
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- | No log | 3.0 | 75 | 0.5479 | 0.8128 | 0.657 | 0.5467 | 0.5781 | 0.8032 |
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- | No log | 4.0 | 100 | 0.5472 | 0.8111 | 0.6286 | 0.5859 | 0.6033 | 0.8063 |
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- | No log | 5.0 | 125 | 0.5590 | 0.8099 | 0.6159 | 0.5816 | 0.5914 | 0.8065 |
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- | No log | 6.0 | 150 | 0.5616 | 0.8131 | 0.5851 | 0.5673 | 0.5756 | 0.8098 |
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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: 0.5163
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+ - Accuracy: 0.8153
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+ - Precision: 0.6449
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+ - Recall: 0.5356
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+ - F1: 0.5682
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+ - Weighted F1: 0.8067
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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 | 0.5489 | 0.8057 | 0.7074 | 0.5047 | 0.5477 | 0.7897 |
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+ | No log | 2.0 | 50 | 0.5456 | 0.811 | 0.6724 | 0.5482 | 0.5813 | 0.8018 |
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+ | No log | 3.0 | 75 | 0.5504 | 0.8148 | 0.6741 | 0.5468 | 0.5885 | 0.8029 |
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+ | No log | 4.0 | 100 | 0.5482 | 0.8123 | 0.644 | 0.5883 | 0.6115 | 0.8073 |
 
 
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  ### Framework versions
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