Feature Extraction
sentence-transformers
Safetensors
Transformers
mistral
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-en-icl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-en-icl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-en-icl") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use BAAI/bge-en-icl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-en-icl")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-en-icl") model = AutoModel.from_pretrained("BAAI/bge-en-icl") - Notebooks
- Google Colab
- Kaggle
Source of training queries of BGE-EN-ICL
#13
by ftvalentini - opened
I have some questions regarding the origin of the training queries used for BGE-EN-ICL, which have no training queries in BEIR:
- quora: 10k test, 5k dev queries in beir -- bge-full-data has 60202 queries.
- scidocsrr: 1k test queries in beir -- bge-full-data has 12654 queries.
- arguana: 1406 test queries in beir -- bge-full-data has 3101 queries.
Where do these train queries come from?
Also for nli dataset: what is the source dataset?
Thank you so much for making such a valuable dataset available!