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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lihuchen/pearl_small") model = AutoModel.from_pretrained("Lihuchen/pearl_small") - Notebooks
- Google Colab
- Kaggle
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README.md
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## PEARL-small
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[Learning High-Quality and General-Purpose Phrase Representations](https://arxiv.org/pdf/2401.10407.pdf). <br>
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[Lihu Chen](https://chenlihu.com), [Gaël Varoquaux](https://gael-varoquaux.info/), [Fabian M. Suchanek](https://suchanek.name/).
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PEARL-small is a
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for phrases and strings. <br>
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If you require semantic similarity computation for strings, our PEARL model might be a helpful tool.<br>
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It offers powerful embeddings suitable for tasks like string matching, entity retrieval, entity clustering, and fuzzy join.
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🤗 [PEARL-small](https://huggingface.co/Lihuchen/pearl_small) 🤗 [PEARL-base](https://huggingface.co/Lihuchen/pearl_base)
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<br>
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## PEARL-small
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[Learning High-Quality and General-Purpose Phrase Representations](https://arxiv.org/pdf/2401.10407.pdf). <br>
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[Lihu Chen](https://chenlihu.com), [Gaël Varoquaux](https://gael-varoquaux.info/), [Fabian M. Suchanek](https://suchanek.name/).
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Accepted by EACL Findings 2024 <br>
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PEARL-small is a lightweight string embedding model. It is the tool of choice for semantic similarity computation for strings,
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creating excellent embeddings for string matching, entity retrieval, entity clustering, fuzzy join...
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<br>
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It differents from typically sentence embedders because it adds a character-level representation giving a good support for open vocabulary.
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The model is a variant of [E5-small](https://huggingface.co/intfloat/e5-small-v2) finetuned on our constructed context-free [dataset](https://zenodo.org/records/10676475) to yield better representations
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for phrases and strings. <br>
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🤗 [PEARL-small](https://huggingface.co/Lihuchen/pearl_small) 🤗 [PEARL-base](https://huggingface.co/Lihuchen/pearl_base)
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<br>
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