Create README.md

README.md

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+---
+license: mit
+datasets:
+- web_nlg
+language:
+- en
+---
+# Model card for Inria-CEDAR/FactSpotter-DeBERTaV3-Small
+
+## Model description
+
+This model is related to the paper **"FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation"**. 
+
+Given a triple of format "subject | predicate | object" and a text, the model determines if the triple is present in the text. 
+
+Different from the paper using ELECTRA, this model is finetuned on DeBERTaV3. 
+
+## How to use the model
+
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+def sentence_cls_score(input_strings, predicate_cls_model, predicate_cls_tokenizer):
+    tokenized_cls_input = predicate_cls_tokenizer(input_strings, truncation=True, padding=True,
+                                                  return_token_type_ids=True)
+    input_ids = torch.Tensor(tokenized_cls_input['input_ids']).long().to(torch.device("cuda"))
+    token_type_ids = torch.Tensor(tokenized_cls_input['token_type_ids']).long().to(torch.device("cuda"))
+    attention_mask = torch.Tensor(tokenized_cls_input['attention_mask']).long().to(torch.device("cuda"))
+    prev_cls_output = predicate_cls_model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
+    softmax_cls_output = torch.softmax(prev_cls_output.logits, dim=1, )
+    return softmax_cls_output
+tokenizer = AutoTokenizer.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Small")
+model = AutoModelForSequenceClassification.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Small")
+# pairs of texts (as premises) and triples (as hypotheses)
+cls_texts = [("the aarhus is the airport of aarhus, denmark", "aarhus airport | city served | aarhus, denmark"),
+             ("aarhus airport is 25.0 metres above the sea level", "aarhus airport | elevation above the sea level | 1174")]
+cls_scores = sentence_cls_score(cls_texts, model, tokenizer)
+# Dimensions: 0-entailment, 1-neutral, 2-contradiction
+label_names = ["entailment", "neutral", "contradiction"]
+```
+## Citation
+If the model is useful to you, please cite the paper
+
+```
+@inproceedings{zhang:hal-04257838,
+  TITLE = {{FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation}},
+  AUTHOR = {Zhang, Kun and Balalau, Oana and Manolescu, Ioana},
+  URL = {https://hal.science/hal-04257838},
+  BOOKTITLE = {{Findings of EMNLP 2023 - Conference on Empirical Methods in Natural Language Processing}},
+  ADDRESS = {Singapore, Singapore},
+  YEAR = {2023},
+  MONTH = Dec,
+  KEYWORDS = {Graph-to-Text Generation ; Factual Faithfulness ; Constrained Text Generation},
+  PDF = {https://hal.science/hal-04257838/file/_EMNLP_2023__Evaluating_the_Factual_Faithfulness_of_Graph_to_Text_Generation_Camera.pdf},
+  HAL_ID = {hal-04257838},
+  HAL_VERSION = {v1},
+}
+```
+
+## Questions
+If you have some questions, please contact through my email [email protected] or [email protected]
+