---
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: 0.7909
- Accuracy: 0.756
- Precision: 0.5226
- Recall: 0.3518
- F1: 0.4027
- Weighted F1: 0.7173

## 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   | 13   | 0.9045          | 0.7251   | 0.3378    | 0.1564 | 0.1457 | 0.6233      |
| No log        | 2.0   | 26   | 0.8688          | 0.7336   | 0.5559    | 0.2281 | 0.2504 | 0.6453      |
| No log        | 3.0   | 39   | 0.8579          | 0.7409   | 0.5851    | 0.2931 | 0.3179 | 0.6795      |
| No log        | 4.0   | 52   | 0.7956          | 0.7507   | 0.5225    | 0.3443 | 0.3919 | 0.7017      |
| No log        | 5.0   | 65   | 0.7947          | 0.7529   | 0.532     | 0.3535 | 0.4026 | 0.7093      |
| No log        | 6.0   | 78   | 0.8063          | 0.7549   | 0.5502    | 0.3752 | 0.4191 | 0.7168      |
| No log        | 7.0   | 91   | 0.8059          | 0.7599   | 0.5496    | 0.3764 | 0.4269 | 0.7227      |


### Framework versions

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