YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model Card for mt5-small en-nl translation
The mt5-small en-nl translation model is a finetuned version of google/mt5-small.
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
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.
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.
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