metadata
library_name: transformers
tags:
- seq2seq
license: apache-2.0
datasets:
- Helsinki-NLP/europarl
- Helsinki-NLP/opus-100
language:
- en
- it
base_model:
- bigscience/mt0-small
pipeline_tag: translation
metrics:
- bleu
🍀 Quadrifoglio - A small model for English -> Italian translation
Quadrifoglio is an encoder-decoder transformer model for English-Italian text translation based on bigscience/mt0-small. It was trained on the en-it section of Helsinki-NLP/opus-100 and Helsinki-NLP/europarl.
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load model and tokenizer from checkpoint directory
tokenizer = AutoTokenizer.from_pretrained("LeonardPuettmann/mt0-Quadrifoglio-mt-en-it")
model = AutoModelForSeq2SeqLM.from_pretrained("LeonardPuettmann/mt0-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)
As this model is trained on translating sentence pairs, it is best to split longer text into individual sentences, ideally using SpaCy:
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/mt0-Quadrifoglio-mt-en-it")
model = AutoModelForSeq2SeqLM.from_pretrained("LeonardPuettmann/mt0-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)
Evaluation
Done on the Opus 100 test set.
BLEU
| Quadrifoglio (this model) | mt0-small | DeepL | |
|---|---|---|---|
| BLEU Score | 0.4816 | 0.0159 | 0.5210 |
| Precision 1 | 0.7305 | 0.2350 | 0.7613 |
| Precision 2 | 0.5413 | 0.0290 | 0.5853 |
| Precision 3 | 0.4289 | 0.0076 | 0.4800 |
| Precision 4 | 0.3417 | 0.0013 | 0.3971 |