How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("feature-extraction", model="ruaccent/RUAccent-encoder")
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("ruaccent/RUAccent-encoder")
model = AutoModel.from_pretrained("ruaccent/RUAccent-encoder")
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RUAccent-encoder

Эта модель представляет собой энкодер для TTS систем, который умеет разрешать омографию. Предназначен для интеграции в TTS системы, но лучше обратите внимание на энкодер который вогнали в стресс (как и автора RUAccent)

Использование

import torch
from transformers import AutoModel
import chartk
tokenizer = chartk.CharacterTokenizer.from_pretrained('ruaccent/RUAccent-encoder')
model = AutoModel.from_pretrained('ruaccent/RUAccent-stressed-encoder')
text = "На горе стоит замок"
inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
with torch.no_grad():
    outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
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Safetensors
Model size
28.7M params
Tensor type
F32
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