README.md

---
license: apache-2.0
widget:
- text: "drawing of tintin in a shop"
  output:
    url: "cfn.svg"
---
# Cross-Encoder for Word Sense Relationships Classification

This model was trained on word sense relationships extracted by WordNet for the [semantic change type classification](https://github.com/ChangeIsKey/change-type-classification).

The model can be used to detect which kind of relatioships (among homonymy, antonymy, hypernonym, hyponymy, and co-hypnomy) intercur between word senses: Given a pair of word sense definitions, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. 

The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)

<b> Citation </b>

```
@inproceedings{change_type_classification_cassotti_2024,
  author    = {Pierluigi Cassotti and
               Stefano De Pascale and
               Nina Tahmasebi},
  title     = {Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types},
  year      = {2024},
}
```


## Usage with Transformers

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')

features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    print(scores)
```


## Usage with SentenceTransformers

The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
labels = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```


## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the 

<Gallery />