Sentence Similarity
Safetensors
sentence-transformers
PyLate
lfm2
liquid
edge
ColBERT
feature-extraction
Eval Results (legacy)
Instructions to use LiquidAI/LFM2-ColBERT-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LiquidAI/LFM2-ColBERT-350M with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="LiquidAI/LFM2-ColBERT-350M") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Metrics in Chinese?
#4
by exoplanet - opened
Neither the blog post nor the model page has any metrics in Chinese. It's okay if the model didn't do well in Chinese, but how bad really, we'd need to know to make a call on whether or not to use this model for Chinese use-cases. Honestly, I expect more transparency from a reputable lab like Liquid AI.
Regarding the chinese metrics, we have extended the nanobeir benchmark for other languages, and we had people available to review the translation in Korean and Japanese. By then, we didn't have anyone to review the translations to Chinese. That was the main reason
fernandofernandes changed discussion status to closed