GIRT-Model: Automated Generation of Issue Report Templates
Paper • 2402.02632 • Published • 1
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
GIRT-Model
paper: https://arxiv.org/abs/2402.02632
demo: https://huggingface.co/spaces/nafisehNik/girt-space
This model is fine-tuned to generate issue report templates based on the input instruction provided. It has been fine-tuned on GIRT-Instruct data.
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# load model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('nafisehNik/girt-t5-base')
tokenizer = AutoTokenizer.from_pretrained(nafisehNik/girt-t5-base)
# method for computing issue report template generation
def compute(sample, top_p, top_k, do_sample, max_length, min_length):
inputs = tokenizer(sample, return_tensors="pt").to('cpu')
outputs = model.generate(
**inputs,
min_length= min_length,
max_length=max_length,
do_sample=do_sample,
top_p=top_p,
top_k=top_k).to('cpu')
generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=False)
generated_text = generated_texts[0]
replace_dict = {
'\n ': '\n',
'</s>': '',
'<pad> ': '',
'<pad>': '',
'<unk>!--': '<!--',
'<unk>': '',
}
postprocess_text = generated_text
for key, value in replace_dict.items():
postprocess_text = postprocess_text.replace(key, value)
return postprocess_text
prompt = "YOUR INPUT INSTRUCTION"
result = compute(prompt, top_p = 0.92, top_k=0, do_sample=True, max_length=300, min_length=30)
Citation
@article{nikeghbal2024girt,
title={GIRT-Model: Automated Generation of Issue Report Templates},
author={Nikeghbal, Nafiseh and Kargaran, Amir Hossein and Heydarnoori, Abbas},
journal={arXiv preprint arXiv:2402.02632},
year={2024}
}
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