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SummaCoz LoRA Adapter for flan-t5-xxl
The model provides summarization factual consistency classificaiton and explanations.
Model Details
- Paper: SummaCoz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization
- Dataset:
nkwbtb/SummaCoz
Model Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xxl")
model = AutoModelForSeq2SeqLM.from_pretrained("nkwbtb/flan-t5-11b-SummaCoz",
torch_dtype="auto",
device_map="auto")
pipe = pipeline("text2text-generation",
model=model,
tokenizer=tokenizer)
PROMPT = """Is the hypothesis true based on the premise? Give your explanation afterwards.
Premise:
{article}
Hypothesis:
{summary}
"""
article = "Goldfish are being caught weighing up to 2kg and koi carp up to 8kg and one metre in length."
summary = "Goldfish are being caught weighing up to 8kg and one metre in length."
print(pipe(PROMPT.format(article=article, summary=summary),
do_sample=False,
max_new_tokens=512))
"""[{'generated_text': '\
No, the hypothesis is not true. \
- The hypothesis states that goldfish are being caught weighing up to 8kg and one metre in length. \
- However, the premise states that goldfish are being caught weighing up to 2kg and koi carp up to 8kg and one metre in length. \
- The difference between the two is that the koi carp is weighing 8kg and the goldfish is weighing 2kg.'}]"""
Citation [optional]
BibTeX:
[More Information Needed]
Model tree for nkwbtb/flan-t5-11b-SummaCoz
Base model
nkwbtb/flan-t5-xxl-bf16