Lihuchen/pearl_base

PEARL-base

Learning High-Quality and General-Purpose Phrase Representations.
Lihu Chen, Gaël Varoquaux, Fabian M. Suchanek.
Accepted by EACL Findings 2024

PEARL-base is a lightweight string embedding model. It is the tool of choice for semantic similarity computation for strings, creating excellent embeddings for string matching, entity retrieval, entity clustering, fuzzy join...
It differs from typical sentence embedders because it incorporates phrase type information and morphological features, allowing it to better capture variations in strings. The model is a variant of E5-base finetuned on our constructed context-free dataset to yield better representations for phrases and strings.

🤗 PEARL-small 🤗 PEARL-base 📐 PEARL Benchmark 🏆 PEARL Leaderboard

Model	Size	Avg	PPDB	PPDB filtered	Turney	BIRD	YAGO	UMLS	CoNLL	BC5CDR	AutoFJ
FastText	-	40.3	94.4	61.2	59.6	58.9	16.9	14.5	3.0	0.2	53.6
Sentence-BERT	110M	50.1	94.6	66.8	50.4	62.6	21.6	23.6	25.5	48.4	57.2
Phrase-BERT	110M	54.5	96.8	68.7	57.2	68.8	23.7	26.1	35.4	59.5	66.9
E5-small	34M	57.0	96.0	56.8	55.9	63.1	43.3	42.0	27.6	53.7	74.8
E5-base	110M	61.1	95.4	65.6	59.4	66.3	47.3	44.0	32.0	69.3	76.1
PEARL-small	34M	62.5	97.0	70.2	57.9	68.1	48.1	44.5	42.4	59.3	75.2
PEARL-base	110M	64.8	97.3	72.2	59.7	72.6	50.7	45.8	39.3	69.4	77.1

Cost comparison of FastText and PEARL. The estimated memory is calculated by the number of parameters (float16). The unit of inference speed is *ms/512 samples. The FastText model here is crawl-300d-2M-subword.bin.

Model	Avg Score	Estimated Memory	Speed GPU	Speed CPU
FastText	40.3	1200MB	-	57ms
PEARL-small	62.5	68MB	42ms	446ms
PEARL-base	64.8	220MB	89ms	1394ms

Usage

Sentence Transformers

PEARL is integrated with the Sentence Transformers library (Thanks for Tom Aarsen's contribution), and can be used like so:

from sentence_transformers import SentenceTransformer, util

query_texts = ["The New York Times"]
doc_texts = [ "NYTimes", "New York Post", "New York"]
input_texts = query_texts + doc_texts

model = SentenceTransformer("Lihuchen/pearl_base")
embeddings = model.encode(input_texts)
scores = util.cos_sim(embeddings[0], embeddings[1:]) * 100
print(scores.tolist())
# [[85.61601257324219, 73.65623474121094, 70.36174774169922]]

Transformers

You can also use transformers to use PEARL. Below is an example of entity retrieval, and we reuse the code from E5.

import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]

def encode_text(model, input_texts):
    # Tokenize the input texts
    batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

    outputs = model(**batch_dict)
    embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
    
    return embeddings


query_texts = ["The New York Times"]
doc_texts = [ "NYTimes", "New York Post", "New York"]
input_texts = query_texts + doc_texts

tokenizer = AutoTokenizer.from_pretrained('Lihuchen/pearl_base')
model = AutoModel.from_pretrained('Lihuchen/pearl_base')

# encode
embeddings = encode_text(model, input_texts)

# calculate similarity
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

# expected outputs
# [[85.61601257324219, 73.65624237060547, 70.36172485351562]]

Training and Evaluation

Have a look at our code on Github

Citation

If you find our work useful, please give us a citation:

@article{chen2024learning,
  title={Learning High-Quality and General-Purpose Phrase Representations},
  author={Chen, Lihu and Varoquaux, Ga{\"e}l and Suchanek, Fabian M},
  journal={arXiv preprint arXiv:2401.10407},
  year={2024}
}