README.md

metadata

license: apache-2.0
language:
  - ru
tags:
  - distill
  - fill-mask
  - embeddings
  - masked-lm
  - tiny
  - sentence-similarity
datasets:
  - GEM/wiki_lingua
  - xnli
  - RussianNLP/wikiomnia
  - mlsum
  - IlyaGusev/gazeta
widget:
  - text: Москва - <mask> России.
  - text: Если б море было пивом, я бы <mask>
  - text: Столица России - <mask>.
library_name: transformers
pipeline_tag: fill-mask

ruRoberta-distilled

Model was distilled from ai-forever/ruRoberta-large with ❤️ by me.

Usage

from transformers import pipeline


pipe = pipeline('feature-extraction', model='d0rj/ruRoberta-distilled')
tokens_embeddings = pipe('Привет, мир!')

import torch
from transformers import AutoTokenizer, AutoModel


tokenizer = AutoTokenizer.from_pretrained('d0rj/ruRoberta-distilled')
model = AutoModel.from_pretrained('d0rj/ruRoberta-distilled')


def embed_bert_cls(text: str) -> torch.Tensor:
    t = tokenizer(text, padding=True, truncation=True, return_tensors='pt').to(model.device)
    with torch.no_grad():
        model_output = model(**t)
    embeddings = model_output.last_hidden_state[:, 0, :]
    embeddings = torch.nn.functional.normalize(embeddings)
    return embeddings[0].cpu()


embedding = embed_bert_cls('Привет, мир!')

Logs

Distillation process lasts for 120 hours on 4 Nvidia V100.

See all logs at WandB.

Configuration changes

• Activation GELU -> GELUFast
• Attention heads 16 -> 8
• Hidden layers 24 -> 6
• Weights size 1.42 GB -> 464 MB

Data

Overall: 9.4 GB of raw texts, 5.1 GB of binarized texts.

Only texts in Russian were used for distillation. I do not know how the model behaves in Englishю

Used data:

• GEM/wiki_lingua
• xnli
• RussianNLP/wikiomnia
• mlsum
• IlyaGusev/gazeta