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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-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
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