Quadrifoglio Mt En It

🚀 四叶草（Quadrifoglio） - 用于英语到意大利语翻译的小型模型

四叶草（Quadrifoglio）是一个基于bigscience/mt0-small的编码器 - 解码器Transformer模型，用于英语和意大利语之间的文本翻译。它在Helsinki-NLP/opus-100和Helsinki-NLP/europarl数据集的英意（en-it）部分上进行了训练。

🚀 快速开始

模型信息

💻 使用示例

基础用法

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)

高级用法

由于该模型是在翻译句子对上进行训练的，因此最好将较长的文本拆分为单个句子，理想情况下可以使用SpaCy。然后可以翻译这些句子，并在最后将翻译结果连接起来，示例代码如下：

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)

📚 详细文档

评估

评估是在Opus 100测试集上完成的。

BLEU评估结果

📄 许可证

本项目采用apache - 2.0许可证。