---
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: 0.1552
- Accuracy: 0.9545
- Precision: 0.8651
- Recall: 0.8306
- F1: 0.8454
- Weighted F1: 0.9544

## 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: 1.8e-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
- lr_scheduler_warmup_ratio: 0.004
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| No log        | 0.5   | 10   | 0.2276          | 0.9252   | 0.7871    | 0.7482 | 0.7616 | 0.9233      |
| No log        | 1.0   | 20   | 0.2124          | 0.9318   | 0.8363    | 0.7571 | 0.7923 | 0.9298      |
| No log        | 1.5   | 30   | 0.2052          | 0.9342   | 0.8199    | 0.794  | 0.8057 | 0.9334      |
| No log        | 2.0   | 40   | 0.1958          | 0.9396   | 0.8132    | 0.8049 | 0.8038 | 0.9384      |
| No log        | 2.5   | 50   | 0.2043          | 0.9385   | 0.8162    | 0.8086 | 0.811  | 0.9377      |
| No log        | 3.0   | 60   | 0.1948          | 0.9409   | 0.8413    | 0.8109 | 0.8249 | 0.9404      |
| No log        | 3.5   | 70   | 0.1951          | 0.9436   | 0.8449    | 0.7963 | 0.8186 | 0.9425      |
| No log        | 4.0   | 80   | 0.2032          | 0.941    | 0.8169    | 0.8158 | 0.8158 | 0.9411      |
| No log        | 4.5   | 90   | 0.1984          | 0.944    | 0.827     | 0.8125 | 0.8194 | 0.9435      |
| No log        | 5.0   | 100  | 0.1982          | 0.9451   | 0.8313    | 0.8072 | 0.8184 | 0.9443      |
| No log        | 5.5   | 110  | 0.1968          | 0.9456   | 0.8249    | 0.8124 | 0.8178 | 0.945       |
| No log        | 6.0   | 120  | 0.2083          | 0.9432   | 0.8113    | 0.8173 | 0.8136 | 0.9429      |
| No log        | 6.5   | 130  | 0.2105          | 0.9441   | 0.8355    | 0.8132 | 0.8236 | 0.9436      |
| No log        | 7.0   | 140  | 0.2083          | 0.9439   | 0.8312    | 0.8207 | 0.8253 | 0.9439      |
| No log        | 7.5   | 150  | 0.2145          | 0.9447   | 0.8293    | 0.8051 | 0.8161 | 0.9437      |


### Framework versions

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