🚀 NegMPNet
NegMPNet is a negation - aware sentence - similarity model. It maps sentences and paragraphs to a 768 - dimensional dense vector space, enabling tasks such as clustering and semantic search.
🚀 Quick Start
NegMPNet is a negation - aware version of all - mpnet - base - v2. It belongs to the sentence - transformers family. This model can map sentences and paragraphs into a 768 - dimensional dense vector space, which is useful for tasks like clustering or semantic search. For more details, refer to our paper This is not correct! Negation - aware Evaluation of Language Generation Systems.
✨ Features
- Negation - awareness: This model has a better sensitivity towards negations compared to its base model.
📦 Installation
Using this model becomes easy when you have sentence - transformers installed:
pip install -U sentence-transformers
💻 Usage Examples
Basic Usage
If you have installed sentence - transformers
, you can use the model as follows:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer("tum - nlp/NegMPNet")
embeddings = model.encode(sentences)
print(embeddings)
Advanced Usage
Negation - awareness Demonstration
This model has a better sensitivity towards negations compared to its base model. You can try it yourself:
from sentence_transformers import SentenceTransformer, util
import torch
base_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
finetuned_model = SentenceTransformer("tum-nlp/NegMPNet")
def cos_similarities(references: list, candidates: list, model: SentenceTransformer, batch_size=8) -> torch.Tensor:
assert len(references) == len(candidates), "Number of references and candidates must be equal"
emb_ref = model.encode(references, batch_size=batch_size)
emb_cand = model.encode(candidates, batch_size=batch_size)
return torch.diag(util.cos_sim(emb_ref, emb_cand))
references = ["Ray charles is legendary.", "Ray charles is legendary"]
candidates = ["Ray charles is a legend.", "Ray charles isn't legendary."]
print(cos_similarities(references, candidates, base_model))
print(cos_similarities(references, candidates, finetuned_model))
Usage without sentence - transformers
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
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
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 = ['This is an example sentence', 'Each sentence is converted']
tokenizer = AutoTokenizer.from_pretrained("tum-nlp/NegMPNet")
model = AutoModel.from_pretrained("tum-nlp/NegMPNet")
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
📚 Documentation
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Training
The model was trained with the following parameters:
DataLoader:
sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader
of length 358 with parameters:
{'batch_size': 64}
Loss:
__main__.MultipleNegativesRankingLoss
with parameters:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
Parameters of the fit() - Method:
{
"epochs": 1,
"evaluation_steps": 35,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 36,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, '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
This model is released under the cc - by - sa - 4.0
license.
📚 Citation
Please cite our INLG 2023 paper, if you use our model.
BibTeX:
@misc{anschütz2023correct,
title={This is not correct! Negation-aware Evaluation of Language Generation Systems},
author={Miriam Anschütz and Diego Miguel Lozano and Georg Groh},
year={2023},
eprint={2307.13989},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
📋 Information Table
Property |
Details |
Model Type |
Sentence - similarity model |
Training Data |
tum - nlp/cannot - dataset |
License |
cc - by - sa - 4.0 |