metadata
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- masakhaner2
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-wolof
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: masakhaner2
type: masakhaner2
config: wol
split: validation
args: wol
metrics:
- name: F1
type: f1
value: 0.8537350910232265
xlm-roberta-base-finetuned-wolof
This model is a fine-tuned version of xlm-roberta-base on the masakhaner2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2257
- F1: 0.8537
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: 4.876283744888558e-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: 7
Training results
Training Loss | Epoch | Step | Validation Loss | F1 |
---|---|---|---|---|
0.4246 | 1.0 | 739 | 0.2225 | 0.7077 |
0.1938 | 2.0 | 1478 | 0.1952 | 0.7762 |
0.1226 | 3.0 | 2217 | 0.1778 | 0.8077 |
0.0748 | 4.0 | 2956 | 0.1746 | 0.8303 |
0.0466 | 5.0 | 3695 | 0.1864 | 0.8419 |
0.0263 | 6.0 | 4434 | 0.2300 | 0.8483 |
0.0137 | 7.0 | 5173 | 0.2257 | 0.8537 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3