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---
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-JNLPBA-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: 0.8224512128396863
- name: Recall
type: recall
value: 0.878188899707887
- name: F1
type: f1
value: 0.8494066679223958
- name: Accuracy
type: accuracy
value: 0.9620705451213926
---
<!-- 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. -->
# electramed-small-JNLPBA-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1167
- Precision: 0.8225
- Recall: 0.8782
- F1: 0.8494
- Accuracy: 0.9621
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.398 | 1.0 | 2087 | 0.1941 | 0.7289 | 0.7936 | 0.7599 | 0.9441 |
| 0.0771 | 2.0 | 4174 | 0.1542 | 0.7734 | 0.8348 | 0.8029 | 0.9514 |
| 0.1321 | 3.0 | 6261 | 0.1413 | 0.7890 | 0.8492 | 0.8180 | 0.9546 |
| 0.2302 | 4.0 | 8348 | 0.1326 | 0.8006 | 0.8589 | 0.8287 | 0.9562 |
| 0.0723 | 5.0 | 10435 | 0.1290 | 0.7997 | 0.8715 | 0.8340 | 0.9574 |
| 0.171 | 6.0 | 12522 | 0.1246 | 0.8115 | 0.8722 | 0.8408 | 0.9593 |
| 0.1058 | 7.0 | 14609 | 0.1204 | 0.8148 | 0.8757 | 0.8441 | 0.9604 |
| 0.1974 | 8.0 | 16696 | 0.1178 | 0.8181 | 0.8779 | 0.8470 | 0.9614 |
| 0.0663 | 9.0 | 18783 | 0.1168 | 0.8239 | 0.8781 | 0.8501 | 0.9620 |
| 0.1022 | 10.0 | 20870 | 0.1167 | 0.8225 | 0.8782 | 0.8494 | 0.9621 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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