---
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