# Using NeyabAI:
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# Fine-Tuning GPT-2 on Custom Dataset:
This repository demonstrates how to fine-tune the GPT-2 language model on a custom dataset using PyTorch and Hugging Face's Transformers library. The code provides an end-to-end example, from loading the dataset to training the model and evaluating its performance.
## Requirements
- Python 3.6+
- PyTorch
- Transformers (Hugging Face)
- NumPy
You can install the required packages using pip:
```bash
pip install torch transformers numpy
```
## Fine-Tuning Script
The following script outlines the steps for fine-tuning GPT-2 on a custom dataset:
```python
from transformers import GPT2LMHeadModel, GPT2TokenizerFast, AdamW
import torch
from torch.utils.data import DataLoader, TensorDataset
import numpy as np
# Load pre-trained model and tokenizer
model_name = "XsoraS/NeyabAI"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2TokenizerFast.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_…
# GPT-2 Based Chatbot (NeyabAI)
This repository demonstrates how to fine-tune the GPT-2 language model on a custom dataset using PyTorch and Hugging Face's Transformers library. The code provides an end-to-end example, from loading the dataset to training the model and evaluating its performance.
## Requirements
- Python 3.6+
- PyTorch
- Transformers (Hugging Face)
- NumPy
You can install the required packages using pip:
```bash
pip install torch transformers numpy
```
## Fine-Tuning Script
The following script outlines the steps for fine-tuning NeyabAI on a custom dataset:
```python
from transformers import GPT2LMHeadModel, GPT2TokenizerFast, AdamW
import torch
from torch.utils.data import DataLoader, TensorDataset
import numpy as np
# Load pre-trained model and tokenizer
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2TokenizerFast.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
# Example dataset
dataset = ["Your…
