Model Card for gemma-2-2b-jpn-it-translate
gemma-2-2b-jpn-it-translateは、googleが公開してくれたgoogle/gemma-2-2b-jpn-itを翻訳タスク用にチューニングしたモデルです
gemma-2-2b-jpn-it-translate is a model tuned for translation tasks based on google/gemma-2-2b-jpn-it released by Google.
パラメーター数は20億（2B）ですが、分野によっては1年前の70億（7B）モデルに迫るレベルの翻訳品質を提供します。ファイルサイズが約5GBと比較的小さいため、高速な実行が可能です。
Although it has 2 billion (2B) parameters, in some fields it provides translation quality approaching that of the 7 billion (7B) model from a year ago. The file size is relatively small at around 5GB, allowing for fast execution.
モデル詳細 Model Details
モデル説明 Model Description
このモデルは、Googleが公開した日本語専用モデル「gemma-2-2b-jpn-it」をファインチューニングしたものです。
特徴として、高速、且つ無限長の文章の翻訳ができる事を目指しました。
具体的には、最初にシステムプロンプト相当の文章（日本語/英語）を与えると、以降はユーザーが入力した文章を翻訳した文章（日本語/英語）を出力するようにトレーニングされています。
また、apply_chat_templateを使用しているため、ミスを誘発しやすいプロンプトテンプレートの手書き作業が不要となっています。
This model is fine-tuned from "gemma-2-2b-jpn-it", a Japanese-specific model released by Google.
Our goal is to translate texts of unlimited length at high speed.
It is trained to output translated text (Japanese/English) in response to user input after being given an initial system prompt-like text (Japanese/English).
Additionally, by using apply_chat_template, it eliminates the need for manual writing of prompt templates, which can be prone to errors.
文単位で翻訳する事を学習しているため、改行を含む長文を一度に渡すと品質が低下します。
長文を翻訳する際は文単位で区切る前処理をしてからモデルに与えてください。
Because the model is trained to translate sentence by sentence, passing a long sentence with line breaks at once will result in a decrease in quality.
When translating a long sentence, please pre-process it by dividing it into sentences before feeding it to the model.
一括翻訳用サンプルColabスクリプト
Googleアカウントをお持ちの方は下記のリンク先で「Open In Colab」ボタンを押すと無料で確かめる事ができます。
If you have a Google account, you can check it out for free by clicking the "Open In Colab" button at the link below.
gemma_2_2b_jpn_it_tranlate_batch_translation_sample.ipynb
日英翻訳用サンプルコード Japanese-English Translation sample script.
GPU付きのパソコンをお持ちの方は以下のスクリプトを参考に動作させる事ができます
GPU付きのパソコンをお持ちでない方はgguf版モデルをお試しください
下記のサンプルスクリプトは文単位に区切る部分の実装が完全なコードではないので用途に合わせて修正してください
If you have a computer with a GPU, you can use the following script as a reference.
If you don't have a computer with a GPU, try the gguf model.
The sample script below does not have a complete implementation of the part that separates sentences, so please modify it to suit your needs.
''' pip install -U transformers '''
import re
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def get_torch_dtype():
if torch.cuda.is_available():
device = torch.device("cuda")
prop = torch.cuda.get_device_properties(device)
# Ampere (Compute Capability 8.0 above), for example L4 support bfloat16, but T4 not support.
if prop.major >= 8:
return torch.bfloat16
return torch.float16
model_name = "webbigdata/gemma-2-2b-jpn-it-translate"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=get_torch_dtype(),
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.unk_token
system_prompt = "You are a highly skilled professional Japanese-English and English-Japanese translator. Translate the given text accurately, taking into account the context and specific instructions provided. Steps may include hints enclosed in square brackets [] with the key and value separated by a colon:. Only when the subject is specified in the Japanese sentence, the subject will be added when translating into English. If no additional instructions or context are provided, use your expertise to consider what the most appropriate context is and provide a natural translation that aligns with that context. When translating, strive to faithfully reflect the meaning and tone of the original text, pay attention to cultural nuances and differences in language usage, and ensure that the translation is grammatically correct and easy to read. After completing the translation, review it once more to check for errors or unnatural expressions. For technical terms and proper nouns, either leave them in the original language or use appropriate translations as necessary. Take a deep breath, calm down, and start translating.\n\n"
instruct = """Translate Japanese to English.\nWhen translating, please use the following hints:\n[writing_style: casual]"""
# 文章を区切る関数
def split_sentences(text):
sentences = []
last = 0
# 句点で文を分割
for match in re.finditer(r'[。！？…]', text):
end = match.end()
# 句点の直後に続く改行を含める
while end < len(text) and text[end] == '\n':
end += 1
sentence = text[last:end]
sentences.append(sentence)
last = end
# 残りのテキストを追加
if last < len(text):
remaining = text[last:]
sentences.append(remaining)
# 各文内の改行を適切に分割
final_sentences = []
for s in sentences:
if '\n' in s:
parts = s.split('\n')
for i, part in enumerate(parts):
if part:
# 最後の部分でなければ改行を追加
if i < len(parts) - 1:
final_sentences.append(part + '\n')
else:
final_sentences.append(part)
# 改行自体を保持
if i < len(parts) - 1:
final_sentences.append('\n')
else:
final_sentences.append(s)
return final_sentences
# 翻訳処理を行う関数
def translate_sentence(sentence, previous_context):
# 過去のコンテキストと新しい文を配列に格納
if sentence.strip() == '':
return sentence
messages = previous_context + [
{"role": "user", "content": sentence}
]
# apply_chat_templateを使用してプロンプトを生成
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to("cuda")
translation = ""
with torch.no_grad():
generated_ids = model.generate(
input_ids=inputs,
num_beams=3, max_new_tokens=1200, do_sample=True, temperature=0.5, top_p=0.3
)
full_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
translation = full_output.split('\nmodel\n')[-1].strip()
return translation
from collections import deque
# メイン処理
def main(text):
sentences = split_sentences(text)
translated_sentences = []
# Initialize context with system prompt
context = deque([
{"role": "user", "content": system_prompt + instruct},
{"role": "assistant", "content": "OK"}
], maxlen=6) # Maximum 10 elements (5 user, 5 assistant)
for i, sentence in enumerate(sentences):
# For the first sentence, use the full context including system prompt
if i == 0:
translation_context = list(context)
else:
# For subsequent sentences, exclude the system prompt
translation_context = list(context)[2:]
translated_sentence = translate_sentence(sentence, translation_context)
translated_sentences.append(translated_sentence)
# Add new interactions to the context
if sentence.strip() != '':
context.append({"role": "user", "content": sentence})
else:
context.append({"role": "user", "content": sentence})
if translated_sentence.strip() != '':
context.append({"role": "assistant", "content": translated_sentence})
else:
context.append({"role": "assistant", "content": translated_sentence})
return translated_sentences
text = """こんにちは。私は田中です。今日はとても良い天気ですね。朝ごはんはパンとコーヒーを食べました。そのあとに散歩に行きました。公園にはたくさんの人がいました。子供たちは遊んでいました。
犬を連れている人もいました。私はベンチに座って本を読みました。風がとても気持ちよかったです。その後、友達とカフェに行きました。
カフェではコーヒーを飲みながらおしゃべりをしました。友達は最近引っ越したばかりだと言いました。新しい家の写真を見せてくれました。
とてもきれいな家でした。時間が経つのがあっという間でした。夕方になり、私は家に帰りました。夕食にはカレーを作りました。カレーはとても美味しかったです。今日一日、とても楽しかったです。"""
translated = main(text)
print(translated)
英日翻訳用サンプルコード English-Japanese Translation sample script.
import re
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def get_torch_dtype():
if torch.cuda.is_available():
device = torch.device("cuda")
prop = torch.cuda.get_device_properties(device)
# Ampere (Compute Capability 8.0 above), for example L4 support bfloat16, but T4 not support.
if prop.major >= 8:
return torch.bfloat16
return torch.float16
model_name = "gemma-2-2b-jpn-it-translate"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=get_torch_dtype(),
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.unk_token
system_prompt = "You are a highly skilled professional Japanese-English and English-Japanese translator. Translate the given text accurately, taking into account the context and specific instructions provided. Steps may include hints enclosed in square brackets [] with the key and value separated by a colon:. Only when the subject is specified in the Japanese sentence, the subject will be added when translating into English. If no additional instructions or context are provided, use your expertise to consider what the most appropriate context is and provide a natural translation that aligns with that context. When translating, strive to faithfully reflect the meaning and tone of the original text, pay attention to cultural nuances and differences in language usage, and ensure that the translation is grammatically correct and easy to read. After completing the translation, review it once more to check for errors or unnatural expressions. For technical terms and proper nouns, either leave them in the original language or use appropriate translations as necessary. Take a deep breath, calm down, and start translating.\n\n"
instruct = """Translate English to Japanese.\nWhen translating, please use the following hints:\n[writing_style: business]"""
# Function to split English sentences
def split_sentences(text):
sentences = []
# Split by newlines, periods, exclamation marks, question marks, or two or more consecutive spaces
pattern = r'(?:\r?\n|\.|\!|\?|(?:\s{2,}))'
splits = re.split(pattern, text)
for split in splits:
split = split.strip()
if split:
sentences.append(split)
return sentences
# Function to translate a sentence
def translate_sentence(sentence, previous_context):
if sentence.strip() == '':
return sentence
messages = previous_context + [
{"role": "user", "content": sentence}
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to("cuda")
translation = ""
with torch.no_grad():
generated_ids = model.generate(
input_ids=inputs,
num_beams=3, max_new_tokens=1200, do_sample=True, temperature=0.5, top_p=0.3
)
full_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
translation = full_output.split('\nmodel\n')[-1].strip()
return translation
from collections import deque
# Main processing function
def main(text):
sentences = split_sentences(text)
translated_sentences = []
context = deque([
{"role": "user", "content": system_prompt + instruct},
{"role": "assistant", "content": "OK"}
], maxlen=6)
for i, sentence in enumerate(sentences):
if i == 0:
translation_context = list(context)
else:
translation_context = list(context)[2:]
translated_sentence = translate_sentence(sentence, translation_context)
translated_sentences.append(translated_sentence)
if sentence.strip() != '':
context.append({"role": "user", "content": sentence})
else:
context.append({"role": "user", "content": sentence})
if translated_sentence.strip() != '':
context.append({"role": "assistant", "content": translated_sentence})
else:
context.append({"role": "assistant", "content": translated_sentence})
return translated_sentences
# Sample English text for translation (business context)
text = """Dear valued clients and partners,
I hope this email finds you well. I am writing to provide you with an important update regarding our company's recent developments and future plans.
Firstly, I am pleased to announce that our Q3 financial results have exceeded expectations, with a 15% increase in revenue compared to the same period last year. This success is largely attributed to the launch of our new product line and the expansion of our services into emerging markets.
In light of this growth, we are planning to implement several strategic initiatives in the coming months:
1. Expansion of our R&D department: We will be investing significantly in research and development to maintain our competitive edge in the market.
2. Sustainability efforts: We are committed to reducing our carbon footprint by 30% over the next five years. This includes transitioning to renewable energy sources and implementing eco-friendly practices across all our operations.
3. Digital transformation: We will be upgrading our IT infrastructure to enhance efficiency and provide better service to our clients.
Additionally, we are excited to announce our upcoming annual conference, which will be held virtually this year due to ongoing global health concerns. The conference will take place on November 15-16, 2024, and will feature keynote speeches from industry leaders, interactive workshops, and networking opportunities.
We value your continued support and partnership. If you have any questions or would like further information about any of these initiatives, please don't hesitate to reach out to your account manager or contact our customer support team.
Thank you for your trust in our company. We look forward to achieving new milestones together.
Best regards,
John Smith
CEO, XYZ Corporation"""
結果 result
['貴社にご愛顧いただき、誠にありがとうございます。', 'このメールがご健在であることを心よりお祈り申し上げます。',
'弊社の最近の進展と今後の計画について、重要なお知らせをご提供いたします。',
'まず、第3四半期の収益が予想を上回ったことをお知らせいたします。昨年の同時期と比較して、売上高が15％増加しました。',
'この成功は、新製品ラインの発売と、新興市場へのサービスの拡大が大きく貢献しています。',
'この成長を踏まえ、今後の数ヶ月にわたって、いくつかの戦略的イニシアティブを実施する予定です。',
'1', 'R＆D部門の拡大：市場での競争力を維持するために、大幅に研究開発に投資する予定です。',
'2', 'サステナビリティの取り組み：次の5年間で、炭素排出量を30%削減することを目指しています。',
'これは、再生可能エネルギー源への移行と、すべての事業活動における環境にやさしい実践の導入を含むものです。',
'3', 'デジタルトランスフォーメーション: 私たちのITインフラを強化し、効率を向上させ、より良いサービスを提供する',
'さらに、今年は新型コロナウイルス感染症の懸念が続くため、オンラインで開催されますが、毎年恒例の年次カンファレンスをお知らせいたします。',
'カンファレンスは2024年11月15日～16日に開催され、業界のリーダーによるキーノートスピーチ、インタラクティブワークショップ、ネットワークングの機会が盛りだくさんです。',
'引き続きご支援とご協力を賜りますようお願い申し上げます。',
'これらのイニシアチブについてご質問がある場合や、さらに詳しい情報をご希望の場合は、ご担当マネジャーにご連絡するか、弊社のカスタマーサポートチームにご連絡ください。',
'弊社の信頼を賜り、誠にありがとうございます。',
'共に新たな目標を達成できることを楽しみにしています。',
'ご清栄のこととお慶び申し上げます。',
'ジョン・スミス', 'XYZ株式会社のCEO']
ベンチマーク結果 Benchmark results
| filename | direction | spBLEU | chrF2++ | comet | xlcomet |
|---|---|---|---|---|---|
| flores200v1 | enja | 28.56 | 37.5 | 0.896 | 0.8512 |
| flores200v1 | jaen | 26.38 | 56.6 | 0.8735 | 0.9324 |
| wmt20 | enja | 15.62 | 29.1 | 0.8796 | 0.7913 |
| wmt20 | jaen | 18.04 | 46.3 | 0.8013 | 0.7815 |
| wmt22 | enja | 17.47 | 30.9 | 0.8799 | 0.8581 |
| wmt22 | jaen | 19.93 | 46.4 | 0.8107 | 0.8821 |
| wmt23 | enja | 15.95 | 28.4 | 0.8585 | 0.8213 |
| wmt23 | jaen | 15.54 | 45.2 | 0.8029 | 0.8669 |
| Business | enja | 21.08 | 38.1 | 0.9041 | 0.9178 |
| Business | jaen | 24.27 | 47.5 | 0.8317 | 0.8716 |
| NTREX128 | enja | 18.18 | 30.1 | 0.8757 | 0.7709 |
謝辞 Acknowledgements
- Developed by: [dahara1@webbigdata]
- Language(s) (NLP): [English, Japanese]
- base model [optional]: gemma-2-2b-jpn-it
BibTeX:
@misc{dahara2024imatrix,
author = {dahara1@webbigdata},
title = {gemma-2-2b-jpn-it-translate: A translation task-specific model based on gemma-2-2b-jpn-it},
year = {2024},
howpublished = {\url{https://huggingface.co/webbigdata/gemma-2-2b-jpn-it-translate/}},
note = {Accessed: 2024-10-10},
abstract = {This model was developed to verify how much Japanese-English and English-Japanese translation performance can be improved with the 2B model.},
}
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