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_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Lihuchen/pearl_base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lihuchen/pearl_base") 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_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lihuchen/pearl_base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lihuchen/pearl_base") model = AutoModel.from_pretrained("Lihuchen/pearl_base") - Notebooks
- Google Colab
- Kaggle
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🤗 [PEARL-small](https://huggingface.co/Lihuchen/pearl_small) 🤗 [PEARL-base](https://huggingface.co/Lihuchen/pearl_base)
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| Model |Size|Avg| PPDB | PPDB filtered |Turney|BIRD|YAGO|UMLS|CoNLL|BC5CDR|AutoFJ|
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🤗 [PEARL-small](https://huggingface.co/Lihuchen/pearl_small) 🤗 [PEARL-base](https://huggingface.co/Lihuchen/pearl_base)
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📐 [PEARL Benchmark](https://huggingface.co/datasets/Lihuchen/pearl_benchmark) 🏆 [PEARL Leaderboard](https://huggingface.co/spaces/Lihuchen/pearl_leaderboard)
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| Model |Size|Avg| PPDB | PPDB filtered |Turney|BIRD|YAGO|UMLS|CoNLL|BC5CDR|AutoFJ|
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