Instructions to use bousejin/xlm-roberta-base-finetuned-panx-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bousejin/xlm-roberta-base-finetuned-panx-fr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bousejin/xlm-roberta-base-finetuned-panx-fr")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bousejin/xlm-roberta-base-finetuned-panx-fr") model = AutoModelForTokenClassification.from_pretrained("bousejin/xlm-roberta-base-finetuned-panx-fr") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.9241871401929781
xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset. It achieves the following results on the evaluation set:
- Loss: 0.1013
- F1: 0.9242
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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.5667 | 1.0 | 191 | 0.2318 | 0.8415 |
| 0.2539 | 2.0 | 382 | 0.1428 | 0.8988 |
| 0.1739 | 3.0 | 573 | 0.1013 | 0.9242 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1