---
license: apache-2.0
language:
- en
tags:
- Phrase Representation
- String Matching
- Fuzzy Join
---
## PEARL-small

[Learning High-Quality and General-Purpose Phrase Representations](https://arxiv.org/pdf/2401.10407.pdf). <br>
[Lihu Chen](chenlihu.com), [Gaël Varoquaux](https://gael-varoquaux.info/), [Fabian M. Suchanek](https://suchanek.name/). 
<br> Accepted by EACL Findings 2024

PEARL-base is finetuned on [E5-base](https://huggingface.co/intfloat/e5-base-v2), 
which can yield better representations for various downstream tasks such as entity clustering, entity retrieval and fuzzy join.

| 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|

## Usage

Below is an example of entity retrieval, and we reuse the code from E5.

```python
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](https://github.com/tigerchen52/PEARL)



## 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}
}
```