|
--- |
|
license: apache-2.0 |
|
language: |
|
- multilingual |
|
- en |
|
- ru |
|
- es |
|
- fr |
|
- de |
|
- it |
|
- pt |
|
- pl |
|
- nl |
|
- vi |
|
- tr |
|
- sv |
|
- id |
|
- ro |
|
- cs |
|
- zh |
|
- hu |
|
- ja |
|
- th |
|
- fi |
|
- fa |
|
- uk |
|
- da |
|
- el |
|
- "no" |
|
- bg |
|
- sk |
|
- ko |
|
- ar |
|
- lt |
|
- ca |
|
- sl |
|
- he |
|
- et |
|
- lv |
|
- hi |
|
- sq |
|
- ms |
|
- az |
|
- sr |
|
- ta |
|
- hr |
|
- kk |
|
- is |
|
- ml |
|
- mr |
|
- te |
|
- af |
|
- gl |
|
- fil |
|
- be |
|
- mk |
|
- eu |
|
- bn |
|
- ka |
|
- mn |
|
- bs |
|
- uz |
|
- ur |
|
- sw |
|
- yue |
|
- ne |
|
- kn |
|
- kaa |
|
- gu |
|
- si |
|
- cy |
|
- eo |
|
- la |
|
- hy |
|
- ky |
|
- tg |
|
- ga |
|
- mt |
|
- my |
|
- km |
|
- tt |
|
- so |
|
- ku |
|
- ps |
|
- pa |
|
- rw |
|
- lo |
|
- ha |
|
- dv |
|
- fy |
|
- lb |
|
- ckb |
|
- mg |
|
- gd |
|
- am |
|
- ug |
|
- ht |
|
- grc |
|
- hmn |
|
- sd |
|
- jv |
|
- mi |
|
- tk |
|
- ceb |
|
- yi |
|
- ba |
|
- fo |
|
- or |
|
- xh |
|
- su |
|
- kl |
|
- ny |
|
- sm |
|
- sn |
|
- co |
|
- zu |
|
- ig |
|
- yo |
|
- pap |
|
- st |
|
- haw |
|
- as |
|
- oc |
|
- cv |
|
- lus |
|
- tet |
|
- gsw |
|
- sah |
|
- br |
|
- rm |
|
- sa |
|
- bo |
|
- om |
|
- se |
|
- ce |
|
- cnh |
|
- ilo |
|
- hil |
|
- udm |
|
- os |
|
- lg |
|
- ti |
|
- vec |
|
- ts |
|
- tyv |
|
- kbd |
|
- ee |
|
- iba |
|
- av |
|
- kha |
|
- to |
|
- tn |
|
- nso |
|
- fj |
|
- zza |
|
- ak |
|
- ada |
|
- otq |
|
- dz |
|
- bua |
|
- cfm |
|
- ln |
|
- chm |
|
- gn |
|
- krc |
|
- wa |
|
- hif |
|
- yua |
|
- srn |
|
- war |
|
- rom |
|
- bik |
|
- pam |
|
- sg |
|
- lu |
|
- ady |
|
- kbp |
|
- syr |
|
- ltg |
|
- myv |
|
- iso |
|
- kac |
|
- bho |
|
- ay |
|
- kum |
|
- qu |
|
- za |
|
- pag |
|
- ngu |
|
- ve |
|
- pck |
|
- zap |
|
- tyz |
|
- hui |
|
- bbc |
|
- tzo |
|
- tiv |
|
- ksd |
|
- gom |
|
- min |
|
- ang |
|
- nhe |
|
- bgp |
|
- nzi |
|
- nnb |
|
- nv |
|
- zxx |
|
- bci |
|
- kv |
|
- new |
|
- mps |
|
- alt |
|
- meu |
|
- bew |
|
- fon |
|
- iu |
|
- abt |
|
- mgh |
|
- mnw |
|
- tvl |
|
- dov |
|
- tlh |
|
- ho |
|
- kw |
|
- mrj |
|
- meo |
|
- crh |
|
- mbt |
|
- emp |
|
- ace |
|
- ium |
|
- mam |
|
- gym |
|
- mai |
|
- crs |
|
- pon |
|
- ubu |
|
- fip |
|
- quc |
|
- gv |
|
- kj |
|
- btx |
|
- ape |
|
- chk |
|
- rcf |
|
- shn |
|
- tzh |
|
- mdf |
|
- ppk |
|
- ss |
|
- gag |
|
- cab |
|
- kri |
|
- seh |
|
- ibb |
|
- tbz |
|
- bru |
|
- enq |
|
- ach |
|
- cuk |
|
- kmb |
|
- wo |
|
- kek |
|
- qub |
|
- tab |
|
- bts |
|
- kos |
|
- rwo |
|
- cak |
|
- tuc |
|
- bum |
|
- cjk |
|
- gil |
|
- stq |
|
- tsg |
|
- quh |
|
- mak |
|
- arn |
|
- ban |
|
- jiv |
|
- sja |
|
- yap |
|
- tcy |
|
- toj |
|
- twu |
|
- xal |
|
- amu |
|
- rmc |
|
- hus |
|
- nia |
|
- kjh |
|
- bm |
|
- guh |
|
- mas |
|
- acf |
|
- dtp |
|
- ksw |
|
- bzj |
|
- din |
|
- zne |
|
- mad |
|
- msi |
|
- mag |
|
- mkn |
|
- kg |
|
- lhu |
|
- ch |
|
- qvi |
|
- mh |
|
- djk |
|
- sus |
|
- mfe |
|
- srm |
|
- dyu |
|
- ctu |
|
- gui |
|
- pau |
|
- inb |
|
- bi |
|
- mni |
|
- guc |
|
- jam |
|
- wal |
|
- jac |
|
- bas |
|
- gor |
|
- skr |
|
- nyu |
|
- noa |
|
- sda |
|
- gub |
|
- nog |
|
- cni |
|
- teo |
|
- tdx |
|
- sxn |
|
- rki |
|
- nr |
|
- frp |
|
- alz |
|
- taj |
|
- lrc |
|
- cce |
|
- rn |
|
- jvn |
|
- hvn |
|
- nij |
|
- dwr |
|
- izz |
|
- msm |
|
- bus |
|
- ktu |
|
- chr |
|
- maz |
|
- tzj |
|
- suz |
|
- knj |
|
- bim |
|
- gvl |
|
- bqc |
|
- tca |
|
- pis |
|
- prk |
|
- laj |
|
- mel |
|
- qxr |
|
- niq |
|
- ahk |
|
- shp |
|
- hne |
|
- spp |
|
- koi |
|
- krj |
|
- quf |
|
- luz |
|
- agr |
|
- tsc |
|
- mqy |
|
- gof |
|
- gbm |
|
- miq |
|
- dje |
|
- awa |
|
- bjj |
|
- qvz |
|
- sjp |
|
- tll |
|
- raj |
|
- kjg |
|
- bgz |
|
- quy |
|
- cbk |
|
- akb |
|
- oj |
|
- ify |
|
- mey |
|
- ks |
|
- cac |
|
- brx |
|
- qup |
|
- syl |
|
- jax |
|
- ff |
|
- ber |
|
- tks |
|
- trp |
|
- mrw |
|
- adh |
|
- smt |
|
- srr |
|
- ffm |
|
- qvc |
|
- mtr |
|
- ann |
|
- kaa |
|
- aa |
|
- noe |
|
- nut |
|
- gyn |
|
- kwi |
|
- xmm |
|
- msb |
|
library_name: transformers |
|
tags: |
|
- text2text-generation |
|
- text-generation-inference |
|
datasets: |
|
- allenai/MADLAD-400 |
|
pipeline_tag: translation |
|
|
|
widget: |
|
- text: "<2en> Como vai, amigo?" |
|
example_title: "Translation to English" |
|
- text: "<2de> Do you speak German?" |
|
example_title: "Translation to German" |
|
|
|
--- |
|
|
|
# Model Card for MADLAD-400-7B-MT |
|
|
|
# Table of Contents |
|
|
|
0. [TL;DR](#TL;DR) |
|
1. [Model Details](#model-details) |
|
2. [Usage](#usage) |
|
3. [Uses](#uses) |
|
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) |
|
5. [Training Details](#training-details) |
|
6. [Evaluation](#evaluation) |
|
7. [Environmental Impact](#environmental-impact) |
|
8. [Citation](#citation) |
|
|
|
# TL;DR |
|
|
|
MADLAD-400-10B-MT is a multilingual machine translation model based on the T5 architecture that was |
|
trained on 250 billion tokens covering over 450 languages using publicly available data. |
|
It is competitive with models that are significantly larger. |
|
|
|
**Disclaimer**: [Juarez Bochi](https://huggingface.co/jbochi), who was not involved in this research, converted |
|
the original weights and wrote the contents of this model card based on the original paper and Flan-T5. |
|
|
|
# Model Details |
|
|
|
## Model Description |
|
|
|
- **Model type:** Language model |
|
- **Language(s) (NLP):** Multilingual (400+ languages) |
|
- **License:** Apache 2.0 |
|
- **Related Models:** [All MADLAD-400 Checkpoints](https://huggingface.co/models?search=madlad) |
|
- **Original Checkpoints:** [All Original MADLAD-400 Checkpoints](https://github.com/google-research/google-research/tree/master/madlad_400) |
|
- **Resources for more information:** |
|
- [Research paper](https://arxiv.org/abs/2309.04662) |
|
- [GitHub Repo](https://github.com/google-research/t5x) |
|
- [Hugging Face MADLAD-400 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/MADLAD-400) - [Pending PR](https://github.com/huggingface/transformers/pull/27471) |
|
|
|
# Usage |
|
|
|
Find below some example scripts on how to use the model: |
|
|
|
## Using the Pytorch model with `transformers` |
|
|
|
### Running the model on a CPU or GPU |
|
|
|
<details> |
|
<summary> Click to expand </summary> |
|
|
|
First, install the Python packages that are required: |
|
|
|
`pip install transformers accelerate sentencepiece` |
|
|
|
```python |
|
from transformers import T5ForConditionalGeneration, T5Tokenizer |
|
|
|
model_name = 'jbochi/madlad400-10b-mt' |
|
model = T5ForConditionalGeneration.from_pretrained(model_name, device_map="auto") |
|
tokenizer = T5Tokenizer.from_pretrained(model_name) |
|
|
|
text = "<2pt> I love pizza!" |
|
input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device) |
|
outputs = model.generate(input_ids=input_ids) |
|
|
|
tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
# Eu adoro pizza! |
|
``` |
|
|
|
</details> |
|
|
|
## Running the model with Candle |
|
|
|
<details> |
|
<summary> Click to expand </summary> |
|
|
|
Usage with [candle](https://github.com/huggingface/candle): |
|
|
|
```bash |
|
$ cargo run --example t5 --release -- \ |
|
--model-id "jbochi/madlad400-10b-mt" \ |
|
--prompt "<2de> How are you, my friend?" \ |
|
--decode --temperature 0 |
|
``` |
|
|
|
</details> |
|
|
|
|
|
# Uses |
|
|
|
## Direct Use and Downstream Use |
|
|
|
> Primary intended uses: Machine Translation and multilingual NLP tasks on over 400 languages. |
|
> Primary intended users: Research community. |
|
|
|
## Out-of-Scope Use |
|
|
|
> 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. |
|
|
|
# Bias, Risks, and Limitations |
|
|
|
> 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. |
|
|
|
## Ethical considerations and risks |
|
|
|
> 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. |
|
|
|
## Known Limitations |
|
|
|
More information needed |
|
|
|
## Sensitive Use: |
|
|
|
More information needed |
|
|
|
# Training Details |
|
|
|
> 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. |
|
|
|
See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. |
|
|
|
## Training Data |
|
|
|
> 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](https://arxiv.org/pdf/2309.04662.pdf). |
|
|
|
## Training Procedure |
|
|
|
See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. |
|
|
|
# Evaluation |
|
|
|
## Testing Data, Factors & Metrics |
|
|
|
> For evaluation, we used WMT, NTREX, Flores-200 and Gatones datasets as described in Section 4.3 in the [paper](https://arxiv.org/pdf/2309.04662.pdf). |
|
|
|
> 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. |
|
|
|
## Results |
|
|
|
 |
|
|
|
 |
|
|
|
 |
|
|
|
See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. |
|
|
|
# Environmental Impact |
|
|
|
More information needed |
|
|
|
# Citation |
|
|
|
**BibTeX:** |
|
|
|
```bibtex |
|
@misc{kudugunta2023madlad400, |
|
title={MADLAD-400: A Multilingual And Document-Level Large Audited Dataset}, |
|
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}, |
|
year={2023}, |
|
eprint={2309.04662}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
