Assamese SFT Dataset v1 (ananddey/assamese-sft-dataset-v1)

An industry-standard, curated, and deduplicated Supervised Fine-Tuning (SFT) dataset for training Assamese language models and conversational AI assistants.

📊 Dataset Summary

• Total Samples: 61,929 instruction-response pairs
• Train Split: 58,833 samples
• Validation Split: 3,096 samples
• Languages: Assamese (as), English (en)
• Primary Use Case: SFT / Instruction Fine-Tuning for generative LLMs (e.g. Gemma, Llama, Mistral)

🏛️ Sources & Composition

1. Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered (~28.7k rows):
   • Single-turn general task instructions, reasoning, translation, and structured task following in Assamese.
2. ai4bharat/indic-align (Dolly_T) (~15.0k rows):
   • Factual Q&A, summarization, creative writing, and text classification in Assamese.
3. ai4bharat/indic-align (OpenAssistant_T) (~19.9k rows):
   • Multi-turn conversational flow, technical explanations, and natural dialogue.
4. Filtering & Deduplication:
   • Filtered for minimum Assamese script ratio (>20%), stripped empty/corrupted entries, and deduplicated using MD5 content hashing (1,706 duplicates removed).

📋 Schema Format

Field	Type	Description
id	string	Unique record identifier (e.g., alpaca_as_023018)
source	string	Provenance dataset tag
system_prompt	string	Assistant persona in Assamese (আপুনি এজন সহায়ক, বিনম্ৰ আৰু সৎ অসমীয়া এআই সহকাৰী।)
messages	list[dict]	Standard OpenAI/HF Chat template ([{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}])
prompt	string	User instruction
response	string	Target completion
text	string	Gemma formatted chat text (<start_of_turn>user\n...<end_of_turn>\n<start_of_turn>model\n...<end_of_turn>)
language	string	"as"
num_turns	int	Number of conversation turns
char_count	int	Total character count

🚀 Usage

Loading with Hugging Face datasets

from datasets import load_dataset

dataset = load_dataset("ananddey/assamese-sft-dataset-v1")
print(dataset["train"][0])

Loading with TRL SFTTrainer

from trl import SFTTrainer
from transformers import TrainingArguments

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
    dataset_text_field="text",
    max_seq_length=2048,
    args=TrainingArguments(
        output_dir="./gemma-4-as-sft",
        learning_rate=1e-4,
        num_train_epochs=2,
    )
)