---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- negation
license: mit
language:
- en
datasets:
- tum-nlp/cannot-dataset
---

# NegMPNet

This is a negation-aware version of [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2).
It is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  
For further information, see our paper [This is not correct! Negation-aware Evaluation of Language Generation Systems](https://arxiv.org/abs/2307.13989).

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
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)
```

## Negation-awareness
This model has a better sensitivity towards negations compared to its base model. You can try it yourself:
```python
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)) # prints tensor([0.9453, 0.8683]) -> no negation-awareness
print(cos_similarities(references, candidates, finetuned_model)) # prints tensor([0.9585, 0.4263]) -> sensitive to negation
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), 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.

```python
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
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("tum-nlp/NegMPNet")
model = AutoModel.from_pretrained("tum-nlp/NegMPNet")

# Tokenize sentences
encoded_input = tokenizer(sentences, 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("Sentence embeddings:")
print(sentence_embeddings)
```



## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})


## Training
The model was trained with the 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})
)
```

## Citation
Please cite our [INLG 2023 paper](https://arxiv.org/abs/2307.13989), if you use our model. 
**BibTeX:**
```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}
}
```