---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- jxner
metrics:
- precision
- recall
- f1
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: medicine-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: jxner
      type: jxner
      config: wnut_17
      split: test
      args: wnut_17
    metrics:
    - type: precision
      value: 0.0
      name: Precision
    - type: recall
      value: 0.0
      name: Recall
    - type: f1
      value: 0.0
      name: F1
    - type: accuracy
      value: 0.859375
      name: Accuracy
---

<!-- 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: 0.7996
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8594

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log        | 1.0   | 1    | 0.8644          | 0.0       | 0.0    | 0.0 | 0.8594   |
| No log        | 2.0   | 2    | 0.7996          | 0.0       | 0.0    | 0.0 | 0.8594   |


### Framework versions

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