|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- ade_drug_effect_ner |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: electramed-small-ADE-DRUG-EFFECT-ner-v3 |
|
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: 0.7436108821104699 |
|
- name: Recall |
|
type: recall |
|
value: 0.6711309523809523 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7055142745404771 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9334986406954859 |
|
--- |
|
|
|
<!-- 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-v3 |
|
|
|
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: 0.1626 |
|
- Precision: 0.7436 |
|
- Recall: 0.6711 |
|
- F1: 0.7055 |
|
- Accuracy: 0.9335 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.3393 | 1.0 | 336 | 0.3055 | 0.6126 | 0.6648 | 0.6376 | 0.9218 | |
|
| 0.2503 | 2.0 | 672 | 0.2138 | 0.7025 | 0.6905 | 0.6964 | 0.9300 | |
|
| 0.1494 | 3.0 | 1008 | 0.1879 | 0.7342 | 0.6555 | 0.6926 | 0.9326 | |
|
| 0.1152 | 4.0 | 1344 | 0.1755 | 0.7323 | 0.6797 | 0.7050 | 0.9327 | |
|
| 0.178 | 5.0 | 1680 | 0.1726 | 0.7279 | 0.6827 | 0.7045 | 0.9326 | |
|
| 0.1814 | 6.0 | 2016 | 0.1654 | 0.7358 | 0.6734 | 0.7032 | 0.9332 | |
|
| 0.1292 | 7.0 | 2352 | 0.1641 | 0.7332 | 0.6849 | 0.7082 | 0.9336 | |
|
| 0.1107 | 8.0 | 2688 | 0.1638 | 0.7520 | 0.6522 | 0.6985 | 0.9337 | |
|
| 0.1911 | 9.0 | 3024 | 0.1625 | 0.7503 | 0.6596 | 0.7020 | 0.9331 | |
|
| 0.1517 | 10.0 | 3360 | 0.1626 | 0.7436 | 0.6711 | 0.7055 | 0.9335 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.22.2 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.5.1 |
|
- Tokenizers 0.12.1 |
|
|
