Instructions to use lijingxin/xlm-roberta-base-finetuned-panx-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lijingxin/xlm-roberta-base-finetuned-panx-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lijingxin/xlm-roberta-base-finetuned-panx-en")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lijingxin/xlm-roberta-base-finetuned-panx-en") model = AutoModelForTokenClassification.from_pretrained("lijingxin/xlm-roberta-base-finetuned-panx-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.7043040804918949
xlm-roberta-base-finetuned-panx-en
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: 0.3814
- F1: 0.7043
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 |
|---|---|---|---|---|
| 1.1472 | 1.0 | 50 | 0.5820 | 0.4600 |
| 0.5186 | 2.0 | 100 | 0.4105 | 0.6645 |
| 0.3599 | 3.0 | 150 | 0.3814 | 0.7043 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3