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---
license: apache-2.0
widget:
- text: "WN results"
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 />
|