metadata
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- uner_dan_ddt
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: uner_dan_ddt
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: uner_dan_ddt
type: uner_dan_ddt
config: default
split: validation
args: default
metrics:
- name: Precision
type: precision
value: 0.9018567639257294
- name: Recall
type: recall
value: 0.8970976253298153
- name: F1
type: f1
value: 0.8994708994708994
- name: Accuracy
type: accuracy
value: 0.9939024390243902
uner_dan_ddt
This model is a fine-tuned version of xlm-roberta-large on the uner_dan_ddt dataset. It achieves the following results on the evaluation set:
- Loss: 0.0333
- Precision: 0.9019
- Recall: 0.8971
- F1: 0.8995
- Accuracy: 0.9939
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: 3e-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: 5.0
Training results
Framework versions
- Transformers 4.31.0
- Pytorch 1.10.1+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3