Text Classification
Transformers
Safetensors
modernbert
language-identification
mmbert
african-languages
text-embeddings-inference
Instructions to use olaverse/lid-neural-608 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use olaverse/lid-neural-608 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="olaverse/lid-neural-608")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("olaverse/lid-neural-608") model = AutoModelForSequenceClassification.from_pretrained("olaverse/lid-neural-608", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval/comparison.csv from olaverse/lid-neural-608: direct link, hf CLI and curl.
- Browser
- Download file 689 Bytes
-
https://huggingface.co/olaverse/lid-neural-608/resolve/main/eval/comparison.csv
- Command line
-
hf download hf://olaverse/lid-neural-608/eval/comparison.csv
-
curl -L -o comparison.csv https://huggingface.co/olaverse/lid-neural-608/resolve/main/eval/comparison.csv
689 Bytes
| ,lid-neural-608 (coverage),lid-neural-608 (traffic),lid-lite-608 (coverage),lid-lite-608 (traffic),GlotLID v3,Meta NLLB LID-218 | |
| "Held-out test, accuracy",0.867,0.861,0.858,0.843,0.783,0.28 | |
| "Held-out test, macro-F1",0.862,0.858,0.856,0.843,0.795,0.182 | |
| "Held-out test, 1-5 words",0.666,0.65,0.672,0.656,0.486,0.218 | |
| Web-weighted accuracy,0.929,0.96,0.932,0.954,0.946,0.901 | |
| "Web-weighted, 1-5 words",0.851,0.906,0.763,0.829,0.808,0.764 | |
| Everyday phrases,0.819,0.943,0.762,0.838,0.857,0.867 | |
| Tatoeba test,0.93,0.926,0.922,0.914,0.868,0.557 | |
| FLORES+ devtest,0.942,0.942,0.934,0.928,0.957,0.874 | |
| FLORES+ first 1-5 words,0.715,0.714,0.708,0.708,0.692,0.618 | |
| UDHR-LID,0.913,0.914,0.91,0.908,0.908,0.567 | |