Model Card for SONAR_200_converted_text_decoder

This is an unofficial port of the multilingual SONAR text decoder (https://huggingface.co/facebook/SONAR) to the transformers format from fairseq2.

Its outputs are expected be equal to those the official implementation (https://github.com/facebookresearch/SONAR), but the latter stays the source of truth.

The decoder supports the same 202 languages as NLLB-200 (see also the source model card and SONAR languages list).

The decoder is currently using custom code for inference that has been tested with transformers==0.5.0.

Encoding texts to vectors

There is another model for this: https://huggingface.co/cointegrated/SONAR_200_text_encode

Decoding embeddings to texts

Below is an example of code that decodes a matrix embs of two text embeddings into French sentences:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.modeling_outputs import BaseModelOutput

tokenizer = AutoTokenizer.from_pretrained("cointegrated/SONAR_200_text_encoder")
# You'll have to trust my remote code. If you don't, inspect and adapt the following file on your own:
# https://huggingface.co/cointegrated/SONAR_200_converted_text_decoder/blob/main/sonar_decoder.py  
decoder = AutoModelForSeq2SeqLM.from_pretrained("cointegrated/SONAR_200_converted_text_decoder", trust_remote_code=True, device_map='cuda')

embs = ??? # generate the embeddings using the SONAR encoder

tokens = decoder.generate(
    # `BaseModelOutput` and `.unsqueeze(1)` are part of the model contract, just do them always
    encoder_outputs=BaseModelOutput(last_hidden_state=embs.unsqueeze(1).to(decoder.device)),
    # see the official repo for the list of all 200 language codes:
    # https://github.com/facebookresearch/SONAR#supported-languages-and-download-links
    forced_bos_token_id=tokenizer.convert_tokens_to_ids("fra_Latn"),
    # in many cases, the generated sentence is much shorter than 256 tokens
    max_length=256,
    # beam search is currently unsupported; only num_beams=1 is valid (although higher values are known to produce better quaity)
    num_beams=1, 
)
print(tokens)
# tensor([[     2, 256057,   7645,   3231,    613,     83,   8449,   6381, 248075,
#               2,      1,      1,      1,      1,      1,      1,      1,      1,
#               1,      1,      1],
#         [     2, 256057,   1048,  33694, 176105,  10184,    591,    812,   2937,
#              33,   4096,     55, 248116,     33,  68190,    440,    901,    832,
#              60, 248075,      2]], device='cuda:0')
texts = tokenizer.decode(out, skip_special_tokens=True)
print(texts)
# ['Mon nom est SONAR.', "Je peux intégrer les phrases dans l'espace vectoriel."]

The embeddings can be generated using the code snippet from the encoder implementation or directly loaded from 2048 numbers:

import torch

embs = torch.tensor(
    [[-0.005285983439534903, 0.002008047653362155, -0.0005622835597023368, 0.006343656685203314, 0.006328853312879801, -0.006190031301230192, 0.0012582960771396756, 0.0011614294489845634, -0.005841733887791634, -0.0033791421446949244, 0.00837445817887783, 0.0033003492280840874, -0.0055628130212426186, 0.0011100503616034985, 0.003932418301701546, 0.007366233505308628, -0.0023557287640869617, 0.004181101452559233, -0.005519371945410967, 0.0016589409206062555, 0.008388850837945938, 0.00904405489563942, 0.007067359983921051, 0.0021222627256065607, -0.0006232558516785502, -0.004447741433978081, -0.00595689145848155, -0.005652812775224447, -0.002746484475210309, -0.00020402959489729255, -0.001324568991549313, 0.006492759566754103, 0.004628376103937626, -0.002164181089028716, 0.0038725179620087147, -0.01218128576874733, 0.0014633119571954012, 0.00516114616766572, 0.003352568717673421, 0.00022478614118881524, -0.002524850657209754, -0.0001422798086423427, 0.00011617605923675, 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)

For advanced examples of usage, please take a look at the readme in https://github.com/facebookresearch/SONAR.