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---
tags:
- generated_from_trainer
datasets:
- ade_drug_dosage_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electramed-small-ADE-DRUG-DOSAGE-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ade_drug_dosage_ner
type: ade_drug_dosage_ner
config: ade
split: train
args: ade
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.8697318007662835
---
<!-- 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-DOSAGE-ner
This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the ade_drug_dosage_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6064
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8697
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.4165 | 1.0 | 14 | 1.3965 | 0.0255 | 0.0636 | 0.0365 | 0.7471 |
| 1.2063 | 2.0 | 28 | 1.1702 | 0.0 | 0.0 | 0.0 | 0.8697 |
| 0.9527 | 3.0 | 42 | 0.9342 | 0.0 | 0.0 | 0.0 | 0.8697 |
| 0.8238 | 4.0 | 56 | 0.7775 | 0.0 | 0.0 | 0.0 | 0.8697 |
| 0.7452 | 5.0 | 70 | 0.6945 | 0.0 | 0.0 | 0.0 | 0.8697 |
| 0.6386 | 6.0 | 84 | 0.6519 | 0.0 | 0.0 | 0.0 | 0.8697 |
| 0.6742 | 7.0 | 98 | 0.6294 | 0.0 | 0.0 | 0.0 | 0.8697 |
| 0.6669 | 8.0 | 112 | 0.6162 | 0.0 | 0.0 | 0.0 | 0.8697 |
| 0.6595 | 9.0 | 126 | 0.6090 | 0.0 | 0.0 | 0.0 | 0.8697 |
| 0.6122 | 10.0 | 140 | 0.6064 | 0.0 | 0.0 | 0.0 | 0.8697 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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