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