Banglat5 Nmt Bn En

🚀 孟加拉語T5機器翻譯模型（bn-en）

本倉庫包含在BanglaNMT 孟加拉語 - 英語數據集上微調後的BanglaT5模型檢查點。該模型可用於孟加拉語和英語之間的翻譯任務，為相關的自然語言處理工作提供了有力支持。

⚠️ 重要提示

預訓練模型使用了特定的歸一化管道，可在此處獲取。為獲得最佳效果，請確保在分詞前使用此庫對文本單元進行歸一化處理。

🚀 快速開始

📦 安裝依賴

pip install transformers
pip install git+https://github.com/csebuetnlp/normalizer

💻 使用示例

基礎用法

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from normalizer import normalize # pip install git+https://github.com/csebuetnlp/normalizer

model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_nmt_bn_en")
tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_nmt_bn_en", use_fast=False)

input_sentence = ""
input_ids = tokenizer(normalize(input_sentence), return_tensors="pt").input_ids
generated_tokens = model.generate(input_ids)
decoded_tokens = tokenizer.batch_decode(generated_tokens)[0]

print(decoded_tokens)

📊 基準測試

• 在BanglaNMT測試集上的表現：

📄 許可證

本項目採用CC BY-NC-SA 4.0許可證。

📖 引用

如果您使用了此模型，請引用以下論文：

@article{bhattacharjee2022banglanlg,
  author    = {Abhik Bhattacharjee and Tahmid Hasan and Wasi Uddin Ahmad and Rifat Shahriyar},
  title     = {BanglaNLG: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla},
  journal   = {CoRR},
  volume    = {abs/2205.11081},
  year      = {2022},
  url       = {https://arxiv.org/abs/2205.11081},
  eprinttype = {arXiv},
  eprint    = {2205.11081}
}

如果您使用了歸一化模塊，請引用以下論文：

@inproceedings{hasan-etal-2020-low,
    title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation",
    author = "Hasan, Tahmid  and
      Bhattacharjee, Abhik  and
      Samin, Kazi  and
      Hasan, Masum  and
      Basak, Madhusudan  and
      Rahman, M. Sohel  and
      Shahriyar, Rifat",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.207",
    doi = "10.18653/v1/2020.emnlp-main.207",
    pages = "2612--2623",
    abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.",
}