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ct2-transformers-converter --model ./madlad400-7b-mt-bt --quantization int8 --output_dir madlad400-7b-mt-bt-ct2-8bit --copy_files added_tokens.json generation_config.json model.safetensors.index.json special_tokens_map.json spiece.model tokenizer.json tokenizer_config.jsonWhile this setup is very likely sub-optimal, we see that back-translation greatly improves en2xx translation (by 3.0 chrf, in the case of Flores-200) in most cases.
transformerspip install transformers accelerate sentencepiece1from transformers import T5ForConditionalGeneration, T5Tokenizer
2
3model_name = 'jbochi/madlad400-7b-mt-bt'
4model = T5ForConditionalGeneration.from_pretrained(model_name, device_map="auto")
5tokenizer = T5Tokenizer.from_pretrained(model_name)
6
7text = "<2pt> I love pizza!"
8input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device)
9outputs = model.generate(input_ids=input_ids)
10
11tokenizer.decode(outputs[0], skip_special_tokens=True)
12# Eu adoro pizza!1$ cargo run --example t5 --release -- \
2 --model-id "jbochi/madlad400-7b-mt-bt" \
3 --prompt "<2de> How are you, my friend?" \
4 --decode --temperature 0Primary intended uses: Machine Translation and multilingual NLP tasks on over 400 languages. Primary intended users: Research community.
These models are trained on general domain data and are therefore not meant to work on domain-specific models out-of-the box. Moreover, these research models have not been assessed for production usecases.
We note that we evaluate on only 204 of the languages supported by these models and on machine translation and few-shot machine translation tasks. Users must consider use of this model carefully for their own usecase.
We trained these models with MADLAD-400 and publicly available data to create baseline models that support NLP for over 400 languages, with a focus on languages underrepresented in large-scale corpora. Given that these models were trained with web-crawled datasets that may contain sensitive, offensive or otherwise low-quality content despite extensive preprocessing, it is still possible that these issues to the underlying training data may cause differences in model performance and toxic (or otherwise problematic) output for certain domains. Moreover, large models are dual use technologies that have specific risks associated with their use and development. We point the reader to surveys such as those written by Weidinger et al. or Bommasani et al. for a more detailed discussion of these risks, and to Liebling et al. for a thorough discussion of the risks of machine translation systems.
We train models of various sizes: a 3B, 32-layer parameter model, a 7.2B 48-layer parameter model and a 10.7B 32-layer parameter model. We share all parameters of the model across language pairs, and use a Sentence Piece Model with 256k tokens shared on both the encoder and decoder side. Each input sentence has a <2xx> token prepended to the source sentence to indicate the target language.
For both the machine translation and language model, MADLAD-400 is used. For the machine translation model, a combination of parallel datasources covering 157 languages is also used. Further details are described in the paper.
For evaluation, we used WMT, NTREX, Flores-200 and Gatones datasets as described in Section 4.3 in the paper.
The translation quality of this model varies based on language, as seen in the paper, and likely varies on domain, though we have not assessed this.
1@misc{kudugunta2023madlad400,
2 title={MADLAD-400: A Multilingual And Document-Level Large Audited Dataset},
3 author={Sneha Kudugunta and Isaac Caswell and Biao Zhang and Xavier Garcia and Christopher A. Choquette-Choo and Katherine Lee and Derrick Xin and Aditya Kusupati and Romi Stella and Ankur Bapna and Orhan Firat},
4 year={2023},
5 eprint={2309.04662},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}