Keyphrase Mpnet V1

🚀 keyphrase-mpnet-v1

This is a sentence-transformers model specialized for phrases. It maps phrases to a 768-dimensional dense vector space and can be used for tasks such as clustering or semantic search. In the original paper, this model is used for calculating semantic-based evaluation metrics of keyphrase models. This model is based on sentence-transformers/all-mpnet-base-v2 and further fine-tuned on 1 million keyphrase data with SimCSE.

🚀 Quick Start

This model is designed to map phrases to a 768-dimensional dense vector space, enabling tasks like clustering and semantic search. It's based on sentence-transformers/all-mpnet-base-v2 and fine - tuned on keyphrase data.

✨ Features

• Specialized for phrases, mapping them to a 768-dimensional dense vector space.
• Applicable for clustering and semantic search tasks.
• Used for calculating semantic-based evaluation metrics of keyphrase models in the original paper.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
phrases = ["information retrieval", "text mining", "natural language processing"]

model = SentenceTransformer('uclanlp/keyphrase-mpnet-v1')
embeddings = model.encode(phrases)
print(embeddings)

Advanced Usage

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
phrases = ["information retrieval", "text mining", "natural language processing"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('uclanlp/keyphrase-mpnet-v1')
model = AutoModel.from_pretrained('uclanlp/keyphrase-mpnet-v1')

# Tokenize sentences
encoded_input = tokenizer(phrases, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Phrase embeddings:")
print(sentence_embeddings)

📚 Documentation

Training

The model is trained on phrases from four keyphrase datasets covering a wide range of domains.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 12, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

📄 License

Citing & Authors

Paper: KPEval: Towards Fine-grained Semantic-based Keyphrase Evaluation

@inproceedings{wu-etal-2024-kpeval,
    title = "{KPE}val: Towards Fine-Grained Semantic-Based Keyphrase Evaluation",
    author = "Wu, Di  and
      Yin, Da  and
      Chang, Kai-Wei",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.117",
    pages = "1959--1981",
}