Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
Phrase Representation
String Matching
Fuzzy Join
Entity Retrieval
text-embeddings-inference
Instructions to use Lihuchen/pearl_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Lihuchen/pearl_small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lihuchen/pearl_small") 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 Lihuchen/pearl_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lihuchen/pearl_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lihuchen/pearl_small") model = AutoModel.from_pretrained("Lihuchen/pearl_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Integrate with Sentence Transformers (#2)
Verified signature- Integrate with Sentence Transformers (071e1bb3c6657116ca95005ca54faa2d8ca39798) - Reduce to Sentence Transformers 2.3.1 (e5f16c718bef58eb279aa75c89541c56103bc4a8)dda363f
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delete sentence transformer config
lihu40a5c25 -
add sentence transformer config
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