Create README.md
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README.md
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---
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license: apache-2.0
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task_categories:
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- question-answering
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- text-generation
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annotations_creators:
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- crowdsourced
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- expert-generated
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language:
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- amh
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- arb
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- ary
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- ars
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- acq
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- arz
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- apc
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- ben
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- ceb
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- dan
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- deu
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- ell
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- eng
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- eus
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- fil
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- fin
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- fra
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- gle
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- guj
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- hat
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- hau
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- hin
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- hun
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- ibo
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- ind
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- ita
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- jav
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- jpn
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- kan
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- kir
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- kor
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- kur
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- lit
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- mal
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- mar
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- mlg
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- msa
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- mya
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- nep
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- nld
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- nso
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- nya
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- pan
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- pes
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- pol
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- por
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- pus
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- rus
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- sin
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- sna
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- snd
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- som
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- spa
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- sqi
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- srp
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- sun
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- swa
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- swe
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- tam
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- tel
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- tha
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- tur
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- ukr
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- urd
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- vie
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- wol
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- xho
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- yor
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- zho
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- zul
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language_creators:
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- crowdsourced
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- expert-generated
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multilinguality:
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- multilingual
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size_categories:
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- 100K<n<1M
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---
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[CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) in ChatML format, ready to use in [HuggingFace TRL's SFT Trainer](https://huggingface.co/docs/trl/main/en/sft_trainer).
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Python code used for conversion:
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```python
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from datasets import load_dataset
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
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dataset = load_dataset("CohereForAI/aya_dataset", split="train")
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| 100 |
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def format(columns):
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| 102 |
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messages = [
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| 103 |
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{
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"role": "user",
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"content": columns["inputs"].strip(),
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},
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{
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"role": "assistant",
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"content": columns["targets"].strip(),
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| 110 |
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},
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| 111 |
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]
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| 112 |
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return { "text": tokenizer.apply_chat_template(messages, tokenize=False) }
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dataset.map(format).select_columns(['text', 'language', 'language_code', 'annotation_type', 'user_id']).to_parquet("train.parquet")
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| 116 |
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```
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