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, 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 нормализации русскоязычных текстов, содержащих математические сущности, в формат LaTeX. Модель является дообученной на переведённом&аугментированном датасете "Mathematics Stack Exchange API Q&A Data" версией модели cointegrated/rut5-small.

Description:

This is a model for mathematical text normalization in Russian, based on the cointegrated/rut5-small paraphraser. The model was created by finetuning the paraphraser on a translated&augmented "Mathematics Stack Exchange API Q&A Data" dataset.

Пример использования:

Usage example:

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from IPython.display import display, Math, Latex

model_dir = "turnipseason/latext5"
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, num_return_sequences=1,
    repetition_penalty=1.2,
    max_length=len(text),
    num_beams=10,
    early_stopping=True
    )
  for h in hypotheses:
    display(Latex(tokenizer.decode(h, skip_special_tokens=True)))

text = '''лямбда прописная квадрат минус три равно десять игрек куб
        При этом шинус икс равен интеграл от экспоненты до трёх игрек штрих'''
get_latex(text)
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