Create README.md

README.md

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+# Denotational Type Classifier
+
+This model classifies the denotational relation between two word senses, represented as a pair of definitions. It is based on `roberta-large` with a sequence-classification head and is trained for lexical-semantic relation classification.
+
+The model predicts one of the following relation types:
+
+* `generalization`
+* `specialization`
+* `metaphor`
+* `metonymy`
+* `homonymy`
+* `antonymy`
+
+The first five labels correspond to the denotational relation types evaluated in SenseRel. `antonymy` is included because it is present in the fine-tuning data, although it is not part of the expert-annotated SenseRel denotational test set.
+
+## Model Details
+
+* **Model type:** Causal language model
+* **Base model:** `meta-llama/Meta-Llama-3.1-8B`
+* **Task:** Denotational relation classification via text generation
+* **Language:** English
+
+## Intended Use
+
+This model is intended for research on lexical semantics, semantic change, polysemy, and sense-level semantic relations. It can be used to classify the relation between two definitions of a word sense, for example whether a newer meaning is a generalization, specialization, metaphorical extension, metonymic extension, homonym, or antonymic development of another meaning.
+
+Example use cases include:
+
+* studying semantic change in dictionaries or lexical resources;
+* analyzing polysemous word senses;
+* supporting sense-level semantic annotation;
+* scaling exploratory studies of denotational change.
+
+The model is not intended for high-stakes decision-making or for general-purpose natural language understanding outside lexical-semantic relation classification.
+
+## Input Format
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "ChangeIsKey/denotational-llama-classifier"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+definition_1 = "the young of the domestic cow"
+definition_2 = "the young of various large mammals"
+
+text = f"{definition_1} <|s|> {definition_2} <|t|>"
+
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    next_token_logits = outputs.logits[0, -1, :]
+
+label_tokens = [
+    "<|generalization|>",
+    "<|specialization|>",
+    "<|metaphor|>",
+    "<|metonymy|>",
+    "<|homonymy|>",
+    "<|antonymy|>",
+]
+
+label_token_ids = tokenizer.convert_tokens_to_ids(label_tokens)
+label_logits = next_token_logits[label_token_ids]
+predicted_id = label_token_ids[torch.argmax(label_logits).item()]
+
+prediction = tokenizer.convert_ids_to_tokens(predicted_id)
+print(prediction)
+```
+
+
+## Training Data
+
+The model was fine-tuned on a combined denotational-relation dataset referred to as `WN+CN+UM`, constructed from existing lexical-semantic resources:
+
+* ChainNet for metaphor, metonymy, and homonymy relations;
+* UniMet for additional metonymy examples;
+* WordNet for generalization, specialization, and auto-antonymy examples.
+  
+These resources use definition or synset-pair information as proxies for relations between word senses.
+
+## Training Procedure
+
+The base Llama model was fine-tuned using a causal language modeling objective.
+
+Each training instance consists of a prompt containing two dictionary definitions followed by the target relation label. During fine-tuning, the model learns to generate the correct label given the input definitions.
+
+## Evaluation
+
+The model was evaluated on:
+
+1. the `WN+CN+UM` test set;
+2. the SenseRel expert-annotated denotational dataset.
+
+| Dataset                       | Weighted F1 |
+| ----------------------------- | ----------: |
+| WN+CN+UM test set             |       0.737 |
+| SenseRel denotational dataset |       0.658 |
+
+On the WN+CN+UM test set, this model achieved the best reported score among the evaluated systems.
+
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@inproceedings{cassotti-etal-2026-senserel,
+    title = "{S}ense{R}el: A Sense-Level Benchmark for Denotational and Connotational Meaning Relations",
+    author = "Cassotti, Pierluigi and
+      Baes, Naomi and
+      De Pascale, Stefano and
+      de S{\'a}, J{\'a}der Martins Camboim and
+      Periti, Francesco and
+      Haslam, Nick and
+      Geeraerts, Dirk and
+      Tahmasebi, Nina",
+    booktitle = "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2026",
+    address = "San Diego, California, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2026.acl-long.20/",
+    pages = "499--515"
+}
+```