Instructions to use LEIA/LEIA-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LEIA/LEIA-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LEIA/LEIA-multilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LEIA/LEIA-multilingual") model = AutoModelForSequenceClassification.from_pretrained("LEIA/LEIA-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 086395ae0663c1a12350dd48c6a52f3cf0e03d70775c97b2fb599b772af46cd4
- Size of remote file:
- 3.82 kB
- SHA256:
- 822d8cf801bfa348d04514d9565ec22e76915c32db40e3844abaed4d059dd960
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.