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Source code for transformers.modeling_marian
# coding=utf-8
# Copyright 2020 Marian Team Authors and The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""PyTorch MarianMTModel model, ported from the Marian C++ repo."""
from transformers.modeling_bart import BartForConditionalGeneration
[docs]class MarianMTModel(BartForConditionalGeneration):
r"""
Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
Model API is identical to BartForConditionalGeneration.
Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
Examples::
from transformers import MarianTokenizer, MarianMTModel
from typing import List
src = 'fr' # source language
trg = 'en' # target language
sample_text = "où est l'arrêt de bus ?"
mname = f'Helsinki-NLP/opus-mt-{src}-{trg}'
model = MarianMTModel.from_pretrained(mname)
tok = MarianTokenizer.from_pretrained(mname)
batch = tok.prepare_translation_batch(src_texts=[sample_text]) # don't need tgt_text for inference
gen = model.generate(**batch) # for forward pass: model(**batch)
words: List[str] = tok.batch_decode(gen, skip_special_tokens=True) # returns "Where is the the bus stop ?"
"""
pretrained_model_archive_map = {} # see https://huggingface.co/models?search=Helsinki-NLP
def prepare_logits_for_generation(self, logits, cur_len, max_length):
logits[:, self.config.pad_token_id] = float("-inf")
if cur_len == max_length - 1 and self.config.eos_token_id is not None:
self._force_token_ids_generation(logits, self.config.eos_token_id)
return logits