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
Download pytorch_model.bin from Lihuchen/pearl_small: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/Lihuchen/pearl_small/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://Lihuchen/pearl_small@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Lihuchen/pearl_small/resolve/refs%2Fpr%2F2/pytorch_model.bin
134 MB
- Xet hash:
- bbfbfb3a64245ac26949eb8b7ad736c4b717d27f3c9dc7ec5f97e4a2b92476af
- Size of remote file:
- 134 MB
- SHA256:
- dd1867e18b7eb11086668e591c725ea5de8716e8998c8f477b244067c1c6df8b
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