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---
library_name: transformers
base_model: dmis-lab/biobert-base-cased-v1.1
tags:
- generated_from_trainer
model-index:
- name: biobert-ner-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-ner-model
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0334
- Compositemention: {'precision': 0.6, 'recall': 0.7714285714285715, 'f1': 0.675, 'number': 35}
- Diseaseclass: {'precision': 0.6206896551724138, 'recall': 0.7142857142857143, 'f1': 0.6642066420664207, 'number': 126}
- Modifier: {'precision': 0.7510204081632653, 'recall': 0.8598130841121495, 'f1': 0.8017429193899782, 'number': 214}
- Specificdisease: {'precision': 0.8186046511627907, 'recall': 0.8543689320388349, 'f1': 0.836104513064133, 'number': 412}
- Overall Precision: 0.7549
- Overall Recall: 0.8297
- Overall F1: 0.7906
- Overall Accuracy: 0.9942
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Compositemention | Diseaseclass | Modifier | Specificdisease | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0302 | 1.0 | 180 | 0.0324 | {'precision': 0.25, 'recall': 0.22857142857142856, 'f1': 0.23880597014925375, 'number': 35} | {'precision': 0.2804878048780488, 'recall': 0.18253968253968253, 'f1': 0.22115384615384615, 'number': 126} | {'precision': 0.5510204081632653, 'recall': 0.6308411214953271, 'f1': 0.588235294117647, 'number': 214} | {'precision': 0.5815217391304348, 'recall': 0.779126213592233, 'f1': 0.6659751037344398, 'number': 412} | 0.5346 | 0.6188 | 0.5736 | 0.9911 |
| 0.0185 | 2.0 | 360 | 0.0241 | {'precision': 0.3958333333333333, 'recall': 0.5428571428571428, 'f1': 0.4578313253012048, 'number': 35} | {'precision': 0.487012987012987, 'recall': 0.5952380952380952, 'f1': 0.5357142857142857, 'number': 126} | {'precision': 0.7046413502109705, 'recall': 0.780373831775701, 'f1': 0.7405764966740577, 'number': 214} | {'precision': 0.7154471544715447, 'recall': 0.8543689320388349, 'f1': 0.7787610619469026, 'number': 412} | 0.6584 | 0.7789 | 0.7136 | 0.9929 |
| 0.012 | 3.0 | 540 | 0.0220 | {'precision': 0.5, 'recall': 0.6285714285714286, 'f1': 0.5569620253164557, 'number': 35} | {'precision': 0.5826086956521739, 'recall': 0.5317460317460317, 'f1': 0.5560165975103734, 'number': 126} | {'precision': 0.6896551724137931, 'recall': 0.8411214953271028, 'f1': 0.7578947368421054, 'number': 214} | {'precision': 0.7658227848101266, 'recall': 0.8810679611650486, 'f1': 0.8194130925507901, 'number': 412} | 0.7069 | 0.8030 | 0.7519 | 0.9942 |
| 0.0092 | 4.0 | 720 | 0.0224 | {'precision': 0.7894736842105263, 'recall': 0.8571428571428571, 'f1': 0.8219178082191781, 'number': 35} | {'precision': 0.5506329113924051, 'recall': 0.6904761904761905, 'f1': 0.6126760563380282, 'number': 126} | {'precision': 0.6920152091254753, 'recall': 0.8504672897196262, 'f1': 0.7631027253668764, 'number': 214} | {'precision': 0.7671840354767184, 'recall': 0.8398058252427184, 'f1': 0.8018539976825029, 'number': 412} | 0.7088 | 0.8196 | 0.7602 | 0.9940 |
| 0.0067 | 5.0 | 900 | 0.0264 | {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 35} | {'precision': 0.5317919075144508, 'recall': 0.7301587301587301, 'f1': 0.6153846153846153, 'number': 126} | {'precision': 0.6977611940298507, 'recall': 0.8738317757009346, 'f1': 0.7759336099585062, 'number': 214} | {'precision': 0.7910798122065728, 'recall': 0.8179611650485437, 'f1': 0.8042959427207637, 'number': 412} | 0.7085 | 0.8183 | 0.7594 | 0.9937 |
| 0.0046 | 6.0 | 1080 | 0.0263 | {'precision': 0.75, 'recall': 0.8571428571428571, 'f1': 0.7999999999999999, 'number': 35} | {'precision': 0.5625, 'recall': 0.7142857142857143, 'f1': 0.6293706293706294, 'number': 126} | {'precision': 0.7357723577235772, 'recall': 0.8457943925233645, 'f1': 0.7869565217391304, 'number': 214} | {'precision': 0.8023529411764706, 'recall': 0.8276699029126213, 'f1': 0.8148148148148149, 'number': 412} | 0.7371 | 0.8158 | 0.7744 | 0.9943 |
| 0.0032 | 7.0 | 1260 | 0.0293 | {'precision': 0.5681818181818182, 'recall': 0.7142857142857143, 'f1': 0.6329113924050633, 'number': 35} | {'precision': 0.5370370370370371, 'recall': 0.6904761904761905, 'f1': 0.6041666666666667, 'number': 126} | {'precision': 0.7215686274509804, 'recall': 0.8598130841121495, 'f1': 0.7846481876332623, 'number': 214} | {'precision': 0.795774647887324, 'recall': 0.8228155339805825, 'f1': 0.8090692124105012, 'number': 412} | 0.7159 | 0.8069 | 0.7587 | 0.9937 |
| 0.002 | 8.0 | 1440 | 0.0294 | {'precision': 0.5416666666666666, 'recall': 0.7428571428571429, 'f1': 0.6265060240963857, 'number': 35} | {'precision': 0.5083798882681564, 'recall': 0.7222222222222222, 'f1': 0.5967213114754099, 'number': 126} | {'precision': 0.7510548523206751, 'recall': 0.8317757009345794, 'f1': 0.7893569844789358, 'number': 214} | {'precision': 0.7736720554272517, 'recall': 0.8131067961165048, 'f1': 0.7928994082840236, 'number': 412} | 0.7023 | 0.8005 | 0.7482 | 0.9939 |
| 0.0007 | 9.0 | 1620 | 0.0300 | {'precision': 0.8648648648648649, 'recall': 0.9142857142857143, 'f1': 0.888888888888889, 'number': 35} | {'precision': 0.6666666666666666, 'recall': 0.7301587301587301, 'f1': 0.696969696969697, 'number': 126} | {'precision': 0.746938775510204, 'recall': 0.8551401869158879, 'f1': 0.7973856209150327, 'number': 214} | {'precision': 0.8393285371702638, 'recall': 0.8495145631067961, 'f1': 0.8443908323281061, 'number': 412} | 0.7849 | 0.8348 | 0.8091 | 0.9946 |
| 0.0037 | 10.0 | 1800 | 0.0314 | {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 35} | {'precision': 0.6312056737588653, 'recall': 0.7063492063492064, 'f1': 0.6666666666666667, 'number': 126} | {'precision': 0.7418032786885246, 'recall': 0.8457943925233645, 'f1': 0.7903930131004366, 'number': 214} | {'precision': 0.8183962264150944, 'recall': 0.8422330097087378, 'f1': 0.8301435406698564, 'number': 412} | 0.7579 | 0.8196 | 0.7875 | 0.9944 |
| 0.0011 | 11.0 | 1980 | 0.0319 | {'precision': 0.7073170731707317, 'recall': 0.8285714285714286, 'f1': 0.7631578947368421, 'number': 35} | {'precision': 0.6148648648648649, 'recall': 0.7222222222222222, 'f1': 0.6642335766423357, 'number': 126} | {'precision': 0.7479674796747967, 'recall': 0.8598130841121495, 'f1': 0.7999999999999999, 'number': 214} | {'precision': 0.8341232227488151, 'recall': 0.8543689320388349, 'f1': 0.8441247002398082, 'number': 412} | 0.7655 | 0.8335 | 0.7981 | 0.9945 |
| 0.0004 | 12.0 | 2160 | 0.0347 | {'precision': 0.6904761904761905, 'recall': 0.8285714285714286, 'f1': 0.7532467532467533, 'number': 35} | {'precision': 0.6328125, 'recall': 0.6428571428571429, 'f1': 0.6377952755905513, 'number': 126} | {'precision': 0.7811158798283262, 'recall': 0.8504672897196262, 'f1': 0.814317673378076, 'number': 214} | {'precision': 0.8028169014084507, 'recall': 0.8300970873786407, 'f1': 0.8162291169451074, 'number': 412} | 0.7648 | 0.8056 | 0.7847 | 0.9942 |
| 0.0014 | 13.0 | 2340 | 0.0334 | {'precision': 0.6, 'recall': 0.7714285714285715, 'f1': 0.675, 'number': 35} | {'precision': 0.6206896551724138, 'recall': 0.7142857142857143, 'f1': 0.6642066420664207, 'number': 126} | {'precision': 0.7510204081632653, 'recall': 0.8598130841121495, 'f1': 0.8017429193899782, 'number': 214} | {'precision': 0.8186046511627907, 'recall': 0.8543689320388349, 'f1': 0.836104513064133, 'number': 412} | 0.7549 | 0.8297 | 0.7906 | 0.9942 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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