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")# pip install -U transformers accelerate # 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
Invoice extractor
#8
by lokaspire - opened
Hello community,
i'm trying to build invoice extractor(layout independent) to extract some specific field, is this able to do with this model and how?
Hello community,
i'm trying to build invoice extractor(layout independent) to extract some specific field, is this able to do with this model and how?
interesting project, are you doing on single file or a large training dataset?.
i have 40-50 invoices of single layout, and we have only 3-4 lauyut as of know
i have 40-50 invoices of single layout, and we have only 3-4 lauyut as of know
@Bondjames Thanks, how do you embed taxanomy within it to process only the most content with most terms from the taxanomy?