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---
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
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