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distilbert-base-uncased-biored-finetuned

Token Classification
Transformers
Safetensors
English
distilbert
low-resource NER
token_classification
biomedicine
medical NER
Generated from Trainer
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Dagobert42 commited on
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1 Parent(s): d61c25d

Push distilbert-base-uncased trained on biored-train_200_splits.pt (200 samples)

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Files changed (3) hide show
  1. README.md +18 -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 [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: 0.5577
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- - Accuracy: 0.8101
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- - Precision: 0.605
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- - Recall: 0.5358
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- - F1: 0.5617
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- - Weighted F1: 0.8043
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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 | 100 | 0.7731 | 0.7459 | 0.5685 | 0.3108 | 0.3467 | 0.6748 |
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- | No log | 2.0 | 200 | 0.6716 | 0.7705 | 0.6073 | 0.4069 | 0.4512 | 0.7384 |
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- | No log | 3.0 | 300 | 0.6072 | 0.7922 | 0.6097 | 0.4576 | 0.5126 | 0.7661 |
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- | No log | 4.0 | 400 | 0.6077 | 0.7965 | 0.6042 | 0.4866 | 0.5285 | 0.7732 |
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- | 0.648 | 5.0 | 500 | 0.5842 | 0.8006 | 0.5972 | 0.5243 | 0.5518 | 0.7937 |
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- | 0.648 | 6.0 | 600 | 0.5688 | 0.8068 | 0.5993 | 0.534 | 0.5611 | 0.8004 |
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- | 0.648 | 7.0 | 700 | 0.5869 | 0.8061 | 0.6039 | 0.5313 | 0.5569 | 0.7958 |
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- | 0.648 | 8.0 | 800 | 0.5740 | 0.8133 | 0.6117 | 0.5324 | 0.5658 | 0.8025 |
 
 
 
 
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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: 0.6868
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+ - Accuracy: 0.7768
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+ - Precision: 0.5392
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+ - Recall: 0.4561
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+ - F1: 0.4898
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+ - Weighted F1: 0.764
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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 | 0.9323 | 0.7124 | 0.3944 | 0.1486 | 0.1309 | 0.5993 |
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+ | No log | 2.0 | 50 | 0.8737 | 0.7248 | 0.5187 | 0.2132 | 0.2341 | 0.6271 |
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+ | No log | 3.0 | 75 | 0.8157 | 0.7353 | 0.4968 | 0.2886 | 0.3314 | 0.6804 |
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+ | No log | 4.0 | 100 | 0.7927 | 0.7452 | 0.5213 | 0.3185 | 0.3686 | 0.6883 |
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+ | No log | 5.0 | 125 | 0.7601 | 0.7507 | 0.5119 | 0.3734 | 0.4161 | 0.7116 |
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+ | No log | 6.0 | 150 | 0.7480 | 0.7555 | 0.5381 | 0.3829 | 0.4285 | 0.718 |
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+ | No log | 7.0 | 175 | 0.7393 | 0.7588 | 0.5393 | 0.4031 | 0.4479 | 0.7272 |
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+ | No log | 8.0 | 200 | 0.7342 | 0.7655 | 0.5512 | 0.4143 | 0.4614 | 0.7363 |
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+ | No log | 9.0 | 225 | 0.7391 | 0.7591 | 0.5262 | 0.4425 | 0.4709 | 0.7395 |
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+ | No log | 10.0 | 250 | 0.7264 | 0.7644 | 0.5332 | 0.4539 | 0.4849 | 0.7484 |
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+ | No log | 11.0 | 275 | 0.7350 | 0.7694 | 0.5419 | 0.452 | 0.4852 | 0.7483 |
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+ | No log | 12.0 | 300 | 0.7389 | 0.77 | 0.5341 | 0.4641 | 0.4921 | 0.752 |
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  ### Framework versions
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