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
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# 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: 0.1527
- Accuracy: 0.9501
- Precision: 0.8539
- Recall: 0.8292
- F1: 0.8405
- Weighted F1: 0.95

## 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   | 0.2071          | 0.9301   | 0.8427    | 0.744  | 0.7851 | 0.9282      |
| No log        | 2.0   | 40   | 0.1945          | 0.9378   | 0.8251    | 0.8091 | 0.8159 | 0.9368      |
| No log        | 3.0   | 60   | 0.1952          | 0.9403   | 0.8461    | 0.8125 | 0.828  | 0.9397      |
| No log        | 4.0   | 80   | 0.1981          | 0.942    | 0.8486    | 0.8211 | 0.8339 | 0.9414      |
| No log        | 5.0   | 100  | 0.2051          | 0.9436   | 0.8376    | 0.8228 | 0.8296 | 0.943       |


### Framework versions

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