first commit

README.md

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+---
+language: en
+widget:
+- text: a 1968 american independent horror film \\n What is Night of the Living Dead?
+---
+
+# QA2Claim Model From ZeroFEC 
+
+ZeroFEC is a faithful and interpetable factual error correction framework introduced in the paper [Zero-shot Faithful Factual Error Correction](https://aclanthology.org/2023.acl-long.311/). It involves a component that converts qa-pairs to declarative statements, which is hosted in this repo. The associated code is released in [this](https://github.com/khuangaf/ZeroFEC) repository.
+
+### How to use
+Using Huggingface pipeline abstraction:
+```python
+from transformers import pipeline
+
+nlp = pipeline("text2text-generation", model='khhuang/zerofec-qa2claim-t5-base', tokenizer='khhuang/zerofec-qa2claim-t5-base')
+    
+QUESTION = "What is Night of the Living Dead?"
+ANSWER = "a 1968 american independent horror film"
+
+def format_inputs(question: str, answer: str):
+    return f"{answer} \\n {question}"
+
+text = format_inputs(QUESTION, ANSWER)
+
+nlp(text)
+# should output [{'generated_text': 'Night of the Living Dead is a 1968 american independent horror film.'}]
+```
+
+Using the pre-trained model directly:
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained('khhuang/zerofec-qa2claim-t5-base')
+model = AutoModelForSeq2SeqLM.from_pretrained('khhuang/zerofec-qa2claim-t5-base')
+
+QUESTION = "What is Night of the Living Dead?"
+ANSWER = "a 1968 american independent horror film"
+
+def format_inputs(question: str, answer: str):
+    return f"{answer} \\n {question}"
+
+text = format_inputs(QUESTION, ANSWER)
+
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+generated_ids = model.generate(input_ids, max_length=32, num_beams=4)
+output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+print(output)
+# should output "Night of the Living Dead is a 1968 american independent horror film."
+```
+
+### Citation
+```
+@inproceedings{huang-etal-2023-zero,
+    title = "Zero-shot Faithful Factual Error Correction",
+    author = "Huang, Kung-Hsiang  and
+      Chan, Hou Pong  and
+      Ji, Heng",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-long.311",
+    doi = "10.18653/v1/2023.acl-long.311",
+    pages = "5660--5676",
+}
+```

config.json

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+      "prefix": "translate English to German: "
+    },
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+  "vocab_size": 32128
+}

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training_args.bin

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