MLDataScientist
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README.md
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license: apache-2.0
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dataset_info:
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features:
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- name: text
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dtype: string
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splits:
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- name: validation
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num_bytes: 642375
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num_examples: 535
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- name: train
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num_bytes: 15585375
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num_examples: 12703
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download_size: 7315916
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dataset_size: 16227750
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configs:
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- config_name: default
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data_files:
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- split: validation
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path: data/validation-*
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- split: train
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path: data/train-*
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---
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---
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license: apache-2.0
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dataset_info:
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features:
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- name: text
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dtype: string
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splits:
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- name: validation
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num_bytes: 642375
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num_examples: 535
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- name: train
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num_bytes: 15585375
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num_examples: 12703
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download_size: 7315916
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dataset_size: 16227750
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configs:
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- config_name: default
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data_files:
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- split: validation
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path: data/validation-*
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- split: train
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path: data/train-*
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---
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# Open Assistant Conversations Dataset Release 2 (OASST2) in Uzbek language
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This dataset is an Uzbek translated version of [OASST2](https://huggingface.co/datasets/OpenAssistant/oasst2) dataset in a thread format with Llama3 chat template.
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Refer to this [translated version](https://huggingface.co/datasets/MLDataScientist/oasst2_uzbek) if you need the original tree format. Otherwise, use this thread format for fine-tuning Llama3 models.
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---
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The Uzbek translation was completed in 45 hours using a single T4 GPU and [nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) model.
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Based on nllb metrics, you might want to only filter out records that were not originally in English or Russian since English-Uzbek and Russian-Uzbek have acceptable metrics and translation quality is noticeable better for those pairs based on my short reviews.
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I am sharing the entire Uzbek translated dataset for future research.
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The following repo and command was used to do the Uzbek translation.
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Repo: https://github.com/UnderstandLingBV/LLaMa2lang
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Command used:
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```!python3 translate.py nllb --model_size 3.3B uzn_Latn output_uzbek --quant8 --base_dataset OpenAssistant/oasst2 --max_length 512 --checkpoint_n 400 --batch_size 40```
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I will fine-tune LLAMA3 8B Uzbek chat model and release in HF soon.
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