metadata

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: []

Dagobert42/xlnet-base-cased-biored-augmented

This model is a fine-tuned version of xlnet-base-cased on the bigbio/biored dataset. It achieves the following results on the evaluation set:

Loss: 0.1510
Accuracy: 0.9508
Precision: 0.8521
Recall: 0.8278
F1: 0.8391
Weighted F1: 0.9506

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	0.2068	0.9335	0.8641	0.7475	0.7976	0.9312
No log	2.0	40	0.1962	0.939	0.8035	0.8046	0.8013	0.9382
No log	3.0	60	0.1965	0.9429	0.8654	0.7947	0.826	0.9415
No log	4.0	80	0.1964	0.9443	0.8279	0.8174	0.8218	0.9436
No log	5.0	100	0.2051	0.9438	0.8441	0.8184	0.8286	0.9435

Framework versions

Transformers 4.35.2
Pytorch 2.0.1+cu117
Datasets 2.18.0
Tokenizers 0.15.0