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README.md

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 ---
 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
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+- "no"
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+- gym
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+- alz
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+- quf
+- luz
+- agr
+- tsc
+- mqy
+- gof
+- gbm
+- miq
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+- 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
+
 ---
+
+# Model Card for MADLAD-400-3B-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-400-3B-MT is a multilingual machine translation model based on the T5 architecture that was
+trained on 1 trillion 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/madlad400-3b-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/madlad400-3b-mt" \
+  --prompt "<2de> How are you, my friend?" \
+  --decode --temperature 0
+```
+
+We also provide a quantized model (1.65 GB vs the original 11.8 GB file):
+
+```
+cargo run --example quantized-t5 --release  -- \
+  --model-id "jbochi/madlad400-3b-mt" --weight-file "model-q4k.gguf" \
+  --prompt "<2de> How are you, my friend?" \
+  --temperature 0
+...
+ Wie geht es dir, mein Freund?
+```
+
+</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
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b7f632037d6452a321fa15/EzsMD1AwCuFH0S0DeD-n8.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b7f632037d6452a321fa15/CJ5zCUVy7vTU76Lc8NZcK.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b7f632037d6452a321fa15/NK0S-yVeWuhKoidpLYh3m.png)
+
+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}
+}
+```
+