transformers pytorch tensorboard electra token-classification generated_from_trainer dataset:bc5_cdr model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- bc5_cdr
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: electramed-small-BC5CDR-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: bc5_cdr
      type: bc5_cdr
      config: BC5CDR-Disease
      split: train
      args: BC5CDR-Disease
    metrics:
    - type: precision
      value: 0.8091945430760605
      name: Precision
    - type: recall
      value: 0.882862677133245
      name: Recall
    - type: f1
      value: 0.8444249427136657
      name: F1
    - type: accuracy
      value: 0.9685851703406814
      name: Accuracy
---

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# electramed-small-BC5CDR-ner

This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the bc5_cdr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1227
- Precision: 0.8092
- Recall: 0.8829
- F1: 0.8444
- Accuracy: 0.9686

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7177        | 1.0   | 286  | 0.6902          | 0.0       | 0.0    | 0.0    | 0.8864   |
| 0.1561        | 2.0   | 572  | 0.3210          | 0.7334    | 0.8104 | 0.7700 | 0.9636   |
| 0.2511        | 3.0   | 858  | 0.2064          | 0.7809    | 0.8711 | 0.8236 | 0.9666   |
| 0.0512        | 4.0   | 1144 | 0.1599          | 0.7937    | 0.8751 | 0.8324 | 0.9689   |
| 0.083         | 5.0   | 1430 | 0.1449          | 0.7983    | 0.8804 | 0.8373 | 0.9679   |
| 0.0412        | 6.0   | 1716 | 0.1315          | 0.8141    | 0.8825 | 0.8469 | 0.9701   |
| 0.1437        | 7.0   | 2002 | 0.1258          | 0.8227    | 0.8758 | 0.8485 | 0.9699   |
| 0.1894        | 8.0   | 2288 | 0.1226          | 0.8141    | 0.8833 | 0.8473 | 0.9696   |
| 0.0236        | 9.0   | 2574 | 0.1220          | 0.8160    | 0.8824 | 0.8479 | 0.9694   |
| 0.0602        | 10.0  | 2860 | 0.1227          | 0.8092    | 0.8829 | 0.8444 | 0.9686   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
