metadata
language:
- ru
- myv
tags:
- erzya
- mordovian
- fill-mask
- pretraining
- embeddings
- masked-lm
- feature-extraction
- sentence-similarity
license: cc-by-sa-4.0
datasets:
- slone/myv_ru_2022
This is an Erzya (myv, cyrillic script) sentence encoder from the paper "The first neural machine translation system for the Erzya language".
It is based on sentence-transformers/LaBSE (license here), but with updated vocabulary and checkpoint:
- Removed all tokens except the most popular ones for English or Russian;
- Added extra tokens for Erzya language;
- Fine-tuned on the slone/myv_ru_2022 corpus using a mixture of tasks:
- Cross-lingual distillation of sentence embeddings from the original LaBSE model, using the parallel
ru-myvcorpus; - Masked language modelling on
myvmonolingual data; - Sentence pair classification to distinguish correct
ru-myvtranslations from random pairs.
- Cross-lingual distillation of sentence embeddings from the original LaBSE model, using the parallel
The model can be used as a sentence encoder or a masked language modelling predictor for Erzya, or fine-tuned for any downstream NLU dask.
Sentence embeddings can be produced with the code below:
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("slone/LaBSE-en-ru-myv-v1")
model = AutoModel.from_pretrained("slone/LaBSE-en-ru-myv-v1")
sentences = ["Hello World", "Привет Мир", "Шумбратадо Мастор"]
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = model_output.pooler_output
embeddings = torch.nn.functional.normalize(embeddings)
print(embeddings.shape) # torch.Size([3, 768])