Instructions to use nielsr/lilt-xlm-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nielsr/lilt-xlm-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nielsr/lilt-xlm-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nielsr/lilt-xlm-roberta-base") model = AutoModel.from_pretrained("nielsr/lilt-xlm-roberta-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
How can I get token level bounding boxes from XLMRobertaTokenizer?
#10 opened over 2 years ago
by
zeroman0112
annotated dutch dataset
2
#9 opened almost 3 years ago
by
Ifyouknowthenyouknow
Invoice extractor
3
#8 opened about 3 years ago
by
lokaspire
How to use a different LM with LiLT?
#7 opened about 3 years ago
by
edxnsn
Training Data
#6 opened over 3 years ago
by
uricamic
Would it be difficult to get a xlm-roberta-large version of this model up?
1
#4 opened over 3 years ago
by
marhlder
Getting TypeError: _batch_encode_plus() got an unexpected keyword argument 'boxes'
1
#1 opened over 3 years ago
by
jimmyloyola