Quadrifoglio Mt En It

🚀 四葉草（Quadrifoglio） - 用於英語到意大利語翻譯的小型模型

四葉草（Quadrifoglio）是一個基於bigscience/mt0-small的編碼器 - 解碼器Transformer模型，用於英語和意大利語之間的文本翻譯。它在Helsinki-NLP/opus-100和Helsinki-NLP/europarl數據集的英意（en-it）部分上進行了訓練。

🚀 快速開始

模型信息

💻 使用示例

基礎用法

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer from checkpoint directory
tokenizer = AutoTokenizer.from_pretrained("LeonardPuettmann/Quadrifoglio-mt-en-it")
model = AutoModelForSeq2SeqLM.from_pretrained("LeonardPuettmann/Quadrifoglio-mt-en-it")

def generate_response(input_text):
    input_ids = tokenizer("translate English to Italian:" + input_text, return_tensors="pt").input_ids
    output = model.generate(input_ids, max_new_tokens=256)
    return tokenizer.decode(output[0], skip_special_tokens=True)

text_to_translate = "I would like a cup of green tea, please."
response = generate_response(text_to_translate)
print(response)

高級用法

由於該模型是在翻譯句子對上進行訓練的，因此最好將較長的文本拆分為單個句子，理想情況下可以使用SpaCy。然後可以翻譯這些句子，並在最後將翻譯結果連接起來，示例代碼如下：

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import spacy
# First, install spaCy and the English language model if you haven't already
# !pip install spacy
# !python -m spacy download en_core_web_sm

nlp = spacy.load("en_core_web_sm")

tokenizer = AutoTokenizer.from_pretrained("LeonardPuettmann/Quadrifoglio-mt-en-it")
model = AutoModelForSeq2SeqLM.from_pretrained("LeonardPuettmann/Quadrifoglio-mt-en-it")

def generate_response(input_text):
    input_ids = tokenizer("translate Italian to English: " + input_text, return_tensors="pt").input_ids
    output = model.generate(input_ids, max_new_tokens=256)
    return tokenizer.decode(output[0], skip_special_tokens=True)

text = "How are you doing? Today is a beautiful day. I hope you are doing fine."
doc = nlp(text)
sentences = [sent.text for sent in doc.sents]

sentence_translations = []
for i, sentence in enumerate(sentences):
    sentence_translation = generate_response(sentence)
    sentence_translations.append(sentence_translation)

full_translation = " ".join(sentence_translations)
print(full_translation)

📚 詳細文檔

評估

評估是在Opus 100測試集上完成的。

BLEU評估結果

📄 許可證

本項目採用apache - 2.0許可證。