Update README.md

# Neverland Products - In-Stock Dataset

This dataset contains product information for in-stock items from the Neverland store. The products range from vape pens to magic mushrooms and other unique items. The data is specifically structured for training and fine-tuning language models, such as Meta-Llama-3.1-8B-Instruct, in text classification, text generation, and other natural language processing (NLP) tasks.

## Dataset Overview

The dataset includes essential information about each product, such as descriptions, sale prices, and variations (e.g., flavors, strains). All products included in this dataset are currently **in-stock**.

### Key Columns:
- **combined_text**: This column concatenates the product description, sale price, and variations into a single text input that can be used for fine-tuning large language models (LLMs). Example format:


- **description**: Detailed description of the product.
- **sale_price**: Discounted sale price of the product (in Thai Baht, ฿).
- **variations**: Different product variations such as flavors or strains.
- **is_in_stock**: A boolean field that confirms the product's availability (only in-stock products are included in this dataset).

## Dataset Columns

| Column | Description |
| ------------- | --------------------------------------------------------------------------- |
| `combined_text` | Concatenation of `description`, `sale_price`, and `variations` for each product |
| `description` | Long form detailed description of the product |
| `sale_price` | Sale price of the product, in Thai Baht (฿) |
| `variations` | Product variations, such as flavors or strains |
| `is_in_stock` | Boolean, indicating whether the product is in stock (only `True` included) |

## Usage

The dataset is formatted for fine-tuning large language models (LLMs) in various tasks, including:
- **Text Generation**: Generate new product descriptions, summaries, or marketing text.
- **Text Classification**: Classify products based on their descriptions and variations (e.g., categorize products into different types).
- **Text Summarization**: Generate shorter summaries or highlights from the detailed product descriptions.

### Example Usage with Hugging Face

You can use this dataset with Hugging Face's `transformers` library to fine-tune LLMs. Here is a sample of how to load and use this dataset for text generation:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained("your-fine-tuned-model")
tokenizer = AutoTokenizer.from_pretrained("your-fine-tuned-model")

# Sample input from the dataset
input_text = "Polaroid Supercolor Magic Mushrooms Chocolate | Sale Price: 1850฿ | Variations: Dark-Choco Mocha Bean, Milk-Choco Hazelnut"

# Tokenize and generate output
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

##Citation


@dataset

{neverland_products_instock_2024,
title = {Neverland Products In-Stock Dataset},
year = {2024},
publisher = {Neverland},
version = {1.0},
url = {https://huggingface.co/datasets/nvl-og/products}

##### Instructions:
1. **Customize**: Replace placeholders like `your-fine-tuned-model` and `[email protected]` with actual values relevant to your project.
2. **Citation**: Adjust the citation format to match your preferred reference format.
3. **Upload**: Place this `README.md` file in the root directory of your dataset or project repository (e.g., on Hugging Face Hub).

README.md

@@ -3,10 +3,10 @@ license: mit
 task_categories:
 - text-classification
 - text-generation
-- token-classification
+- summarization
 language:
 - en
-pretty_name: Neverland Products
+pretty_name: Neverland Product
 tags:
 - ecommerce
 - text-generation