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license: mit
tags:
- generated_from_trainer
widget:
- text: "Tachycardia is an increased heart rate for any reason. It can be a usual rise in heart rate caused by exercise or a stress response. Sinus tachycardia is considered a symptom, not a disease."
example_title: "Tachycardia"
- text: "Tuberclosis generally damages the lungs, but it can also impair other parts of the body such as brain and spine. Typical signs of active Tuberclosis include chronic cought with blood-containing mucus, fever, night sweats and weight loss. Tuberclosis damages the lungs whereas Malaria could detriment both kidneys by imparing the liver."
example_title: "Tuberculosis"
- text: "Cholera is a bacterial disease usually spread through contaminated water. A bacterium called Vibrio cholerae causes cholera infection. Symptoms of cholera infection can include: Diarrhea, Nausea, vomiting, Dehydration. Risk factors for cholera include: Poor sanitary conditions, Household exposure, Type O blood, Raw or undercooked shellfish."
example_title: "Cholera"
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-Disease-NER
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. -->
# roberta-base-Disease-NER
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7496
- Precision: 0.5450
- Recall: 0.6759
- F1: 0.6035
- Accuracy: 0.8198
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 180 | 0.7775 | 0.3892 | 0.5483 | 0.4552 | 0.7676 |
| No log | 2.0 | 360 | 0.5731 | 0.4717 | 0.6003 | 0.5283 | 0.8152 |
| 0.8746 | 3.0 | 540 | 0.5629 | 0.4745 | 0.6515 | 0.5491 | 0.8164 |
| 0.8746 | 4.0 | 720 | 0.5848 | 0.4603 | 0.6744 | 0.5472 | 0.8106 |
| 0.8746 | 5.0 | 900 | 0.5489 | 0.5212 | 0.6686 | 0.5858 | 0.8239 |
| 0.4396 | 6.0 | 1080 | 0.5524 | 0.5123 | 0.6804 | 0.5845 | 0.8195 |
| 0.4396 | 7.0 | 1260 | 0.5550 | 0.5001 | 0.6842 | 0.5778 | 0.8174 |
| 0.4396 | 8.0 | 1440 | 0.5787 | 0.4982 | 0.6882 | 0.5780 | 0.8128 |
| 0.3302 | 9.0 | 1620 | 0.5824 | 0.5104 | 0.6939 | 0.5882 | 0.8154 |
| 0.3302 | 10.0 | 1800 | 0.5872 | 0.5295 | 0.6781 | 0.5947 | 0.8211 |
| 0.3302 | 11.0 | 1980 | 0.6047 | 0.5261 | 0.6867 | 0.5957 | 0.8210 |
| 0.2564 | 12.0 | 2160 | 0.6151 | 0.5357 | 0.6739 | 0.5969 | 0.8220 |
| 0.2564 | 13.0 | 2340 | 0.6560 | 0.5204 | 0.6784 | 0.5890 | 0.8172 |
| 0.204 | 14.0 | 2520 | 0.6866 | 0.5162 | 0.6919 | 0.5913 | 0.8155 |
| 0.204 | 15.0 | 2700 | 0.6994 | 0.5192 | 0.6887 | 0.5921 | 0.8145 |
| 0.204 | 16.0 | 2880 | 0.6904 | 0.5309 | 0.6764 | 0.5949 | 0.8199 |
| 0.1655 | 17.0 | 3060 | 0.7752 | 0.4925 | 0.6919 | 0.5754 | 0.8059 |
| 0.1655 | 18.0 | 3240 | 0.7464 | 0.5182 | 0.6832 | 0.5893 | 0.8152 |
| 0.1655 | 19.0 | 3420 | 0.7739 | 0.5242 | 0.6784 | 0.5914 | 0.8157 |
| 0.1335 | 20.0 | 3600 | 0.7496 | 0.5450 | 0.6759 | 0.6035 | 0.8198 |
| 0.1335 | 21.0 | 3780 | 0.7835 | 0.5296 | 0.6759 | 0.5939 | 0.8141 |
| 0.1335 | 22.0 | 3960 | 0.8174 | 0.5080 | 0.6869 | 0.5841 | 0.8092 |
| 0.1155 | 23.0 | 4140 | 0.8307 | 0.5336 | 0.6746 | 0.5959 | 0.8153 |
| 0.1155 | 24.0 | 4320 | 0.8457 | 0.5253 | 0.6832 | 0.5939 | 0.8126 |
| 0.0959 | 25.0 | 4500 | 0.8473 | 0.5250 | 0.6829 | 0.5936 | 0.8138 |
| 0.0959 | 26.0 | 4680 | 0.8971 | 0.5131 | 0.6837 | 0.5862 | 0.8069 |
| 0.0959 | 27.0 | 4860 | 0.8770 | 0.5229 | 0.6849 | 0.5930 | 0.8161 |
| 0.0814 | 28.0 | 5040 | 0.9317 | 0.5012 | 0.6894 | 0.5804 | 0.8083 |
| 0.0814 | 29.0 | 5220 | 0.9051 | 0.5288 | 0.6776 | 0.5940 | 0.8141 |
| 0.0814 | 30.0 | 5400 | 0.9387 | 0.5184 | 0.6839 | 0.5897 | 0.8106 |
| 0.0706 | 31.0 | 5580 | 0.9402 | 0.5261 | 0.6897 | 0.5969 | 0.8134 |
| 0.0706 | 32.0 | 5760 | 0.9603 | 0.5121 | 0.6839 | 0.5857 | 0.8104 |
| 0.0706 | 33.0 | 5940 | 0.9535 | 0.5255 | 0.6769 | 0.5917 | 0.8145 |
| 0.062 | 34.0 | 6120 | 0.9675 | 0.5250 | 0.6844 | 0.5942 | 0.8142 |
| 0.062 | 35.0 | 6300 | 0.9938 | 0.5249 | 0.6754 | 0.5907 | 0.8128 |
| 0.062 | 36.0 | 6480 | 0.9890 | 0.5222 | 0.6796 | 0.5906 | 0.8124 |
| 0.0544 | 37.0 | 6660 | 1.0106 | 0.5244 | 0.6794 | 0.5919 | 0.8135 |
| 0.0544 | 38.0 | 6840 | 1.0285 | 0.5230 | 0.6839 | 0.5928 | 0.8109 |
| 0.0489 | 39.0 | 7020 | 1.0253 | 0.5219 | 0.6809 | 0.5909 | 0.8137 |
| 0.0489 | 40.0 | 7200 | 1.0263 | 0.5229 | 0.6806 | 0.5914 | 0.8124 |
| 0.0489 | 41.0 | 7380 | 1.0511 | 0.5205 | 0.6849 | 0.5915 | 0.8113 |
| 0.0447 | 42.0 | 7560 | 1.0563 | 0.5145 | 0.6804 | 0.5859 | 0.8110 |
| 0.0447 | 43.0 | 7740 | 1.0521 | 0.5210 | 0.6814 | 0.5905 | 0.8128 |
| 0.0447 | 44.0 | 7920 | 1.0581 | 0.5220 | 0.6799 | 0.5906 | 0.8115 |
| 0.0411 | 45.0 | 8100 | 1.0597 | 0.5221 | 0.6816 | 0.5913 | 0.8127 |
| 0.0411 | 46.0 | 8280 | 1.0770 | 0.5216 | 0.6844 | 0.5920 | 0.8114 |
| 0.0411 | 47.0 | 8460 | 1.0689 | 0.5275 | 0.6847 | 0.5959 | 0.8128 |
| 0.039 | 48.0 | 8640 | 1.0665 | 0.5284 | 0.6821 | 0.5955 | 0.8135 |
| 0.039 | 49.0 | 8820 | 1.0715 | 0.5271 | 0.6829 | 0.5950 | 0.8128 |
| 0.0374 | 50.0 | 9000 | 1.0716 | 0.5273 | 0.6827 | 0.5950 | 0.8130 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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