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---
license: mit
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pubmedbert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: jnlpba
type: jnlpba
config: jnlpba
split: train
args: jnlpba
metrics:
- name: Precision
type: precision
value: 0.6877153861747415
- name: Recall
type: recall
value: 0.7833063957515586
- name: F1
type: f1
value: 0.7324050086355786
- name: Accuracy
type: accuracy
value: 0.926729986431479
---
<!-- 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. -->
# pubmedbert-finetuned-ner
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3766
- Precision: 0.6877
- Recall: 0.7833
- F1: 0.7324
- Accuracy: 0.9267
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1607 | 1.0 | 2319 | 0.2241 | 0.6853 | 0.7835 | 0.7311 | 0.9302 |
| 0.112 | 2.0 | 4638 | 0.2620 | 0.6753 | 0.7929 | 0.7294 | 0.9276 |
| 0.0785 | 3.0 | 6957 | 0.3014 | 0.6948 | 0.7731 | 0.7319 | 0.9268 |
| 0.055 | 4.0 | 9276 | 0.3526 | 0.6898 | 0.7801 | 0.7322 | 0.9268 |
| 0.0418 | 5.0 | 11595 | 0.3766 | 0.6877 | 0.7833 | 0.7324 | 0.9267 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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