---
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-finetuned
  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-finetuned

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: 0.7341
- Accuracy: 0.7714
- Precision: 0.5341
- Recall: 0.4169
- F1: 0.4594
- Weighted F1: 0.7495

## 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   | 25   | 0.8831          | 0.7377   | 0.4536    | 0.2481 | 0.2822 | 0.6719      |
| No log        | 2.0   | 50   | 0.8309          | 0.7542   | 0.6035    | 0.3177 | 0.3598 | 0.6933      |
| No log        | 3.0   | 75   | 0.7695          | 0.7624   | 0.568     | 0.3566 | 0.409  | 0.7189      |
| No log        | 4.0   | 100  | 0.7562          | 0.7676   | 0.5536    | 0.3886 | 0.4398 | 0.7343      |
| No log        | 5.0   | 125  | 0.7540          | 0.7673   | 0.5711    | 0.4013 | 0.4474 | 0.7368      |
| No log        | 6.0   | 150  | 0.7425          | 0.7754   | 0.5867    | 0.4398 | 0.4873 | 0.7514      |
| No log        | 7.0   | 175  | 0.7806          | 0.7788   | 0.606     | 0.4235 | 0.473  | 0.7475      |
| No log        | 8.0   | 200  | 0.7638          | 0.7785   | 0.558     | 0.4547 | 0.4871 | 0.7549      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.0