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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, 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
Описание:
Модель для преобразования стиля и восстановления разметки для образовательных математических текстов в формат LaTeX. Модель является дообученной на переведённом&аугментированном датасете "Mathematics Stack Exchange API Q&A Data" версией модели sshleifer/distilbart-cnn-12-6 .
Пример использования:
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from IPython.display import display, Math, Latex
model_dir = "kostyabuh21/DistilBART_forLaTeX "
model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
def get_latex(text):
inputs = tokenizer(text, return_tensors='pt').to(device)
with torch.no_grad():
hypotheses = model.generate(
**inputs,
do_sample=True,
top_p=0.95,
num_return_sequences=1,
repetition_penalty=1.2,
max_length=len(text),
temperature=0.6,
min_length=10,
length_penalty=1.0,
no_repeat_ngram_size=2
)
for h in hypotheses:
display(Latex(tokenizer.decode(h, skip_special_tokens=True)))
print(tokenizer.decode(h, skip_special_tokens=True))
text = 'интеграл от 3 до 5 по икс dx'
get_latex(text)
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