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---
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
config: ncbi_disease
split: test
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.8604187437686939
- name: Recall
type: recall
value: 0.8989583333333333
- name: F1
type: f1
value: 0.879266428935303
- name: Accuracy
type: accuracy
value: 0.9870188186308527
---
<!-- 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-mnli-snli-scinli-scitail-mednli-stsb-ncbi
This model is a fine-tuned version of [pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb](https://huggingface.co/pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0814
- Precision: 0.8604
- Recall: 0.8990
- F1: 0.8793
- Accuracy: 0.9870
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 340 | 0.0481 | 0.8308 | 0.8438 | 0.8372 | 0.9840 |
| 0.0715 | 2.0 | 680 | 0.0497 | 0.8337 | 0.8771 | 0.8548 | 0.9857 |
| 0.0152 | 3.0 | 1020 | 0.0588 | 0.8596 | 0.8802 | 0.8698 | 0.9858 |
| 0.0152 | 4.0 | 1360 | 0.0589 | 0.8589 | 0.8875 | 0.8730 | 0.9873 |
| 0.0059 | 5.0 | 1700 | 0.0693 | 0.8412 | 0.8938 | 0.8667 | 0.9852 |
| 0.003 | 6.0 | 2040 | 0.0770 | 0.8701 | 0.9 | 0.8848 | 0.9863 |
| 0.003 | 7.0 | 2380 | 0.0787 | 0.861 | 0.8969 | 0.8786 | 0.9863 |
| 0.0014 | 8.0 | 2720 | 0.0760 | 0.8655 | 0.8979 | 0.8814 | 0.9872 |
| 0.0007 | 9.0 | 3060 | 0.0817 | 0.8589 | 0.8938 | 0.8760 | 0.9865 |
| 0.0007 | 10.0 | 3400 | 0.0814 | 0.8604 | 0.8990 | 0.8793 | 0.9870 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
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