Indictrans2 Indic En Dist 200M

🚀 IndicTrans2

IndicTrans2是一個用於印度語到英語翻譯的模型，支持多種印度語言，在多語言翻譯任務中表現出色，為印度地區的語言交流和信息傳播提供了有力的支持。

🚀 快速開始

請參考TMLR提交文檔中的第7.6節：蒸餾模型，以獲取有關模型訓練、數據和指標的更多詳細信息。

如需瞭解如何使用與Hugging Face兼容的IndicTrans2模型進行推理，請參考GitHub倉庫中的詳細說明。

💻 使用示例

基礎用法

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from IndicTransToolkit.processor import IndicProcessor
# recommended to run this on a gpu with flash_attn installed
# don't set attn_implemetation if you don't have flash_attn
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

src_lang, tgt_lang = "hin_Deva", "eng_Latn"
model_name = "ai4bharat/indictrans2-indic-en-dist-200M"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

model = AutoModelForSeq2SeqLM.from_pretrained(
    model_name, 
    trust_remote_code=True, 
    torch_dtype=torch.float16, # performance might slightly vary for bfloat16
    attn_implementation="flash_attention_2"
).to(DEVICE)

ip = IndicProcessor(inference=True)

input_sentences = [
    "जब मैं छोटा था, मैं हर रोज़ पार्क जाता था।",
    "हमने पिछले सप्ताह एक नई फिल्म देखी जो कि बहुत प्रेरणादायक थी।",
    "अगर तुम मुझे उस समय पास मिलते, तो हम बाहर खाना खाने चलते।",
    "मेरे मित्र ने मुझे उसके जन्मदिन की पार्टी में बुलाया है, और मैं उसे एक तोहफा दूंगा।",
]

batch = ip.preprocess_batch(
    input_sentences,
    src_lang=src_lang,
    tgt_lang=tgt_lang,
)

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Tokenize the sentences and generate input encodings
inputs = tokenizer(
    batch,
    truncation=True,
    padding="longest",
    return_tensors="pt",
    return_attention_mask=True,
).to(DEVICE)

# Generate translations using the model
with torch.no_grad():
    generated_tokens = model.generate(
        **inputs,
        use_cache=True,
        min_length=0,
        max_length=256,
        num_beams=5,
        num_return_sequences=1,
    )

# Decode the generated tokens into text
generated_tokens = tokenizer.batch_decode(
    generated_tokens,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=True,
)

# Postprocess the translations, including entity replacement
translations = ip.postprocess_batch(generated_tokens, lang=tgt_lang)

for input_sentence, translation in zip(input_sentences, translations):
    print(f"{src_lang}: {input_sentence}")
    print(f"{tgt_lang}: {translation}")

📚 詳細文檔

📢 長上下文IT2模型

• 基於新RoPE的IndicTrans2模型能夠處理長達2048個標記的序列長度，可在此處獲取。
• 只需更改model_name參數即可使用這些模型。有關生成的更多信息，請閱讀RoPE - IT2模型的模型卡片。
• 建議使用flash_attention_2運行這些模型，以實現高效生成。

📄 許可證

本項目採用MIT許可證。

📚 引用

如果您考慮使用我們的工作，請使用以下引用：

@article{gala2023indictrans,
title={IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
author={Jay Gala and Pranjal A Chitale and A K Raghavan and Varun Gumma and Sumanth Doddapaneni and Aswanth Kumar M and Janki Atul Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M Khapra and Raj Dabre and Anoop Kunchukuttan},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=vfT4YuzAYA},
note={}
}

📋 模型信息