It was finetuned on 237k rows of the iwslt2017 dataset and roughly 38k rows of the opus_books dataset. The model was trained for 15 epochs with a batchsize of 16.

How to use

Install dependencies

bash

pip install transformers
pip install sentencepiece
pip install protobuf

You can use the following code for model inference. This model was finetuned to work with an identifier when prompted that needs to be present for the best results.

Python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig

# load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Michielo/mt5-small_en-nl_translation")
model = AutoModelForSeq2SeqLM.from_pretrained("Michielo/mt5-small_en-nl_translation")

# tokenize input
inputs = tokenizer(">>nl<< Your English text here", return_tensors="pt")
# calculate the output
outputs = model.generate(**inputs)
# decode and print
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))

Benchmarks

You can replicate our benchmark scores here without writing any code yourself.

Benchmark	Score
BLEU	43.63%
chr-F	62.25%
chr-F++	61.87%

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.