metadata
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned-xlm-roberta-base-NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
config: ncbi_disease
split: test
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.7974434611602753
- name: Recall
type: recall
value: 0.8447916666666667
- name: F1
type: f1
value: 0.8204350025290845
- name: Accuracy
type: accuracy
value: 0.9804874066212189
finetuned-xlm-roberta-base-NER
This model is a fine-tuned version of xlm-roberta-base on the ncbi_disease dataset. It achieves the following results on the evaluation set:
- Loss: 0.0589
- Precision: 0.7974
- Recall: 0.8448
- F1: 0.8204
- Accuracy: 0.9805
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 340 | 0.0809 | 0.6839 | 0.8698 | 0.7657 | 0.9723 |
| 0.1092 | 2.0 | 680 | 0.0589 | 0.7974 | 0.8448 | 0.8204 | 0.9805 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0