transformers safetensors xlnet token-classification low-resource NER token_classification biomedicine medical NER generated_from_trainer en dataset:medicine base_model:xlnet/xlnet-base-cased base_model:finetune:xlnet/xlnet-base-cased license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: xlnet-base-cased
datasets:
- medicine
language:
- en
license: mit
metrics:
- accuracy
- precision
- recall
- f1
tags:
- low-resource NER
- token_classification
- biomedicine
- medical NER
- generated_from_trainer
model-index:
- name: Dagobert42/xlnet-base-cased-biored-augmented-super
  results: []
---

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

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: 0.2035
- Accuracy: 0.9315
- Precision: 0.8447
- Recall: 0.8503
- F1: 0.8469
- Weighted F1: 0.9318

## 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   | 0.2497          | 0.9156   | 0.8595    | 0.7951 | 0.8242 | 0.9144      |
| No log        | 2.0   | 50   | 0.2404          | 0.9215   | 0.843     | 0.838  | 0.8404 | 0.9213      |
| No log        | 3.0   | 75   | 0.2595          | 0.9142   | 0.82      | 0.8571 | 0.8369 | 0.9161      |
| No log        | 4.0   | 100  | 0.2448          | 0.9266   | 0.8539    | 0.8261 | 0.8396 | 0.9257      |


### Framework versions

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