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---
tags:
- generated_from_trainer
datasets:
- ade_drug_effect_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-ADE-DRUG-EFFECT-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ade_drug_effect_ner
type: ade_drug_effect_ner
config: ade
split: train
args: ade
metrics:
- name: Precision
type: precision
value: 0.7745054945054946
- name: Recall
type: recall
value: 0.6555059523809523
- name: F1
type: f1
value: 0.7100544025790851
- name: Accuracy
type: accuracy
value: 0.9310355073540336
---
<!-- 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-ADE-DRUG-EFFECT-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ade_drug_effect_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1630
- Precision: 0.7745
- Recall: 0.6555
- F1: 0.7101
- Accuracy: 0.9310
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4498 | 1.0 | 336 | 0.3042 | 0.5423 | 0.6295 | 0.5826 | 0.9114 |
| 0.2572 | 2.0 | 672 | 0.2146 | 0.7596 | 0.6194 | 0.6824 | 0.9276 |
| 0.1542 | 3.0 | 1008 | 0.1894 | 0.7806 | 0.6168 | 0.6891 | 0.9299 |
| 0.1525 | 4.0 | 1344 | 0.1771 | 0.7832 | 0.625 | 0.6952 | 0.9309 |
| 0.1871 | 5.0 | 1680 | 0.1723 | 0.7271 | 0.6920 | 0.7091 | 0.9304 |
| 0.1425 | 6.0 | 2016 | 0.1683 | 0.7300 | 0.6979 | 0.7136 | 0.9297 |
| 0.1638 | 7.0 | 2352 | 0.1654 | 0.7432 | 0.6771 | 0.7086 | 0.9306 |
| 0.1592 | 8.0 | 2688 | 0.1635 | 0.7613 | 0.6585 | 0.7062 | 0.9305 |
| 0.1882 | 9.0 | 3024 | 0.1625 | 0.7858 | 0.6373 | 0.7038 | 0.9309 |
| 0.1339 | 10.0 | 3360 | 0.1630 | 0.7745 | 0.6555 | 0.7101 | 0.9310 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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