---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- jxner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: medicine-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: jxner
      type: jxner
      config: wnut_17
      split: test
      args: wnut_17
    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: 0.9
---

<!-- 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. -->

# medicine-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the jxner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5562
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 1    | 1.7398          | 0.0370    | 0.125  | 0.0571 | 0.65     |
| No log        | 2.0   | 2    | 1.5750          | 0.0       | 0.0    | 0.0    | 0.86     |
| No log        | 3.0   | 3    | 1.4146          | 0.0       | 0.0    | 0.0    | 0.88     |
| No log        | 4.0   | 4    | 1.2611          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 5.0   | 5    | 1.1173          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 6.0   | 6    | 0.9869          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 7.0   | 7    | 0.8737          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 8.0   | 8    | 0.7804          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 9.0   | 9    | 0.7074          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 10.0  | 10   | 0.6545          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 11.0  | 11   | 0.6181          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 12.0  | 12   | 0.5938          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 13.0  | 13   | 0.5780          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 14.0  | 14   | 0.5682          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 15.0  | 15   | 0.5623          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 16.0  | 16   | 0.5589          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 17.0  | 17   | 0.5571          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 18.0  | 18   | 0.5563          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 19.0  | 19   | 0.5562          | 0.0       | 0.0    | 0.0    | 0.9      |
| No log        | 20.0  | 20   | 0.5562          | 0.0       | 0.0    | 0.0    | 0.9      |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2