talhakk/agriculture-qa-tokenized

🌾 Agriculture-QA Tokenized Dataset (Gemma Ready)

🔍 Overview

The Agriculture-QA Tokenized Dataset is a high-performance, ready-to-train version of the original agriculture-qa dataset. It has been specifically optimized for Large Language Models (LLMs) like Gemma, LLaMA, and Mistral.

It contains 25,410 high-quality question-answer pairs transformed into instruction-style sequences and pre-tokenized for causal language modeling ($CLM$). This removes the preprocessing bottleneck, allowing you to jump straight into fine-tuning.

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🚀 Key Features

• ✅ 25K+ Agriculture QA Pairs: Comprehensive domain coverage.
• ✅ Gemma-Compatible: Pre-tokenized using the Gemma tokenizer.
• ✅ Instruction-Tuned Format: Structured specifically for Question: [text] \n Answer: [text].
• ✅ Efficiency: No padding applied (enabling dynamic padding during training for 2x faster throughput).
• ✅ Optimized for LoRA/QLoRA: Plug-and-play for PEFT libraries.

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🧠 Data Structure

Each entry is a dictionary containing the necessary tensors for training:

Field	Description
input_ids	Tokenized sequence of Question + Answer
attention_mask	Mask to avoid performing attention on padding
labels	The target sequence (identical to input_ids for Causal LM)

Format Example:

Question: How to improve wheat yield? > Answer: Improve soil fertility through balanced NPK application...

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⚙️ Preprocessing Pipeline

• Tokenizer: google/gemma-2b (Transformers)
• Max Length: 512 tokens
• Truncation: Enabled
• Padding: None (Recommended: apply dynamic padding at runtime)
• Parallelization: Multi-core processed for integrity

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📊 Dataset Statistics

Feature	Value
Total Samples	25,410
Format	Tokenized / Instruction-Style
Max Sequence Length	512
Language	English
Base Model	Gemma

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🧪 Quick Start (Usage)

Load the dataset

from datasets import load_dataset

dataset = load_dataset("talhakk/agriculture-qa-tokenized")
print(dataset["train"][0])