README.md

---
language:
- en
license: mit
base_model: xlnet-base-cased
tags:
- low-resource NER
- token_classification
- biomedicine
- medical NER
- generated_from_trainer
datasets:
- medicine
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Dagobert42/xlnet-base-cased-biored-augmented
  results: []
---

<!-- 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. -->

# Dagobert42/xlnet-base-cased-biored-augmented

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1510
- Accuracy: 0.9508
- Precision: 0.8521
- Recall: 0.8278
- F1: 0.8391
- Weighted F1: 0.9506

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log        | 1.0   | 20   | 0.2068          | 0.9335   | 0.8641    | 0.7475 | 0.7976 | 0.9312      |
| No log        | 2.0   | 40   | 0.1962          | 0.939    | 0.8035    | 0.8046 | 0.8013 | 0.9382      |
| No log        | 3.0   | 60   | 0.1965          | 0.9429   | 0.8654    | 0.7947 | 0.826  | 0.9415      |
| No log        | 4.0   | 80   | 0.1964          | 0.9443   | 0.8279    | 0.8174 | 0.8218 | 0.9436      |
| No log        | 5.0   | 100  | 0.2051          | 0.9438   | 0.8441    | 0.8184 | 0.8286 | 0.9435      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.0