This is a ported version of the BLASER quality estimation (REF) model originally developed in BLASER: Bilingual Language-Agnostic Sentence Representations.

Ported to Hugging Face Transformers: no dependency on Fairseq.
Uses embeddings from the ported SONAR 200 multilingual text encoder (cointegrated/SONAR_200_text_encoder).
Supports the same 202 languages as SONAR / NLLB-200.
Outputs BLASER scores on a 1–5 scale for a source–MT–REF triplet.

⚠️ This is not the original implementation. Attribution goes to the original BLASER authors.

How to compute Ref scores

python

# !pip install transformers sentencepiece  torch -q
import torch
from transformers import AutoTokenizer, AutoModel
from transformers.models.m2m_100.modeling_m2m_100 import M2M100Encoder

# 1. Load SONAR encoder
sonar_model_name = "cointegrated/SONAR_200_text_encoder"
encoder = M2M100Encoder.from_pretrained(sonar_model_name)
tokenizer = AutoTokenizer.from_pretrained(sonar_model_name)

def encode_mean_pool(texts, tokenizer, encoder, lang='eng_Latn', norm=False):
    tokenizer.src_lang = lang
    with torch.inference_mode():
        batch = tokenizer(texts, return_tensors='pt', padding=True)
        seq_embs = encoder(**batch).last_hidden_state
        mask = batch.attention_mask
        mean_emb = (seq_embs * mask.unsqueeze(-1)).sum(1) / mask.unsqueeze(-1).sum(1)
        if norm:
            mean_emb = torch.nn.functional.normalize(mean_emb)
    return mean_emb

# Example sentences
src_sentences = ["Le chat s'assit sur le tapis."]
mt_sentences = ["The cat sat down on the carpet."]  # Example MT output
ref_sentences = ["The cat sat on the mat."]  # Example reference translation

# Encode source and MT sentences
src_embs = encode_mean_pool(src_sentences, tokenizer, encoder, lang="fra_Latn")
mt_embs = encode_mean_pool(mt_sentences, tokenizer, encoder, lang="eng_Latn")
ref_embs = encode_mean_pool(ref_sentences, tokenizer, encoder, lang="eng_Latn")

# 2. Load BLASER Ref model (ported)
ref_model_name = "oist/blaser_2_0_ref_ported"
ref_model = AutoModel.from_pretrained(ref_model_name, trust_remote_code=True)
ref_model.eval()  # set to evaluation mode

# 3. Compute Ref scores
with torch.inference_mode():
    ref_scores = ref_model(src_embs, mt_embs, ref_embs)  # expects source and MT embeddings, and ref embeddings
    print("Blaser score shape:", ref_scores.shape)
    print("Blaser scores:", ref_scores[0])