Source code for transformers.modeling_marian

# coding=utf-8
# Copyright 2020 Marian Team Authors and The HuggingFace Inc. team.
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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