---
license: apache-2.0
widget:
- text: "अपने अनुप्रयोग को पहुंचनीयता व्यायाम"
- text: "जनतंत्र की सफलता केवल इस बात से नहीं हो सकती है कि हर"
- text: "अगर इसके बाद भी वे फैसले पर कायम रहते हैं और"
- text: "मामले का खुलासा होने के बाद"
- text: "My name is Julien and I like to"
- text: "My name is Thomas and my main"
inference:
  parameters:
    max_length: 200
---

# Model Overview: 
The model is a language generation model designed for extending the GPT2 models to support Hindi language along with the original languages that it supports. It was fine-tuned on Hindi texts of [wikipedia](https://www.kaggle.com/datasets/disisbig/hindi-wikipedia-articles-55k) articles.

# Model Architecture and Parameters: 
The model architecture is based on the GPT-2 framework, specifically using the parameters of the small version of the original OpenAI GPT2 model. It employs a Byte Pair Encoding (BPE) tokenizer.

# Corpus: 
The training corpus for Hindi GPT2 consists of Wikipedia articles.

# Tokenizer: 
A tokenizer is trained on Hindi Wikipedia Corpus. The new tokenizer vocabulary (5000 tokens) is merged with existing tokenizer. Hindi GPT2 uses a byte-level version of Byte Pair Encoding (BPE) for tokenizing Hindi text, including Unicode characters. The tokenizer has a vocabulary size of 53497, which allows it to effectively represent the Hindi language's rich vocabulary. Input sequences are formed by breaking the text into consecutive tokens with a maximum length of 1024 tokens.

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

More information needed

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Step | Training Loss | Validation Loss |
| :---- | :------------- | :--------------- |
| 500  | 2.0016        | 1.066703        |
| 1000 | 1.0314        | 0.959653        |
| 1500 | 0.9593        | 0.918827        |
| 2000 | 0.922         | 0.889607        |
| 2500 | 0.8983        | 0.872523        |
| 3000 | 0.8852        | 0.863592        |


### Framework versions

- Transformers 4.30.2
- torch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.3