add model

README.md

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+---
+license: apache-2.0
+widget:
+- text: "drawing of tintin in a shop"
+  output:
+    url: "https://github.com/ChangeIsKey/change-type-classification/blob/main/lsc_ctd_benchmark_snippet_table.png"
+---
+# 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 />