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-super
    results: []

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

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: 0.2035
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