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---
title: "Indonesian GPT2 Medical Chatbot"
tags:
- gpt2
base_model:
- indonesian-nlp/gpt2
datasets:
- Danda245/medical-qa-id-llama-split
---
# Indonesian GPT2 Medical Chatbot
This model is a fine-tuned version of GPT-2 for medical chatbot in the Indonesian language. It can be used to generate answers to various medical questions.
#### Model Sources
- **Repository:** [indonesian-nlp/gpt2](https://huggingface.co/indonesian-nlp/gpt2)
#### Dataset
- **Repository:** [Danda245/medical-qa-id-llama-split](https://huggingface.co/datasets/Danda245/medical-qa-id-llama-split)
| Metric | Value |
|----------------------------------|------------------------------|
| **Global Steps** | 14480 |
| **Training Loss** | 0.3822 |
| **Training Runtime** | 15150.72 seconds |
| **Training Samples per Second** | 11.47 samples/sec |
| **Training Steps per Second** | 0.96 steps/sec |
| **Total FLOPs** | 4.54e+16 |
| **Epoch** | 5.0 |
#### How to Use
```bash
!pip install transformers torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "lafarizo/indo_medical_gpt2_v2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
input_text = input("Pertanyaan: ")
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
input_ids = inputs['input_ids'].to(device)
attention_mask = inputs['attention_mask'].to(device)
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_length=512,
num_beams=5,
temperature=0.7,
top_k=50,
top_p=0.9,
no_repeat_ngram_size=2,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id
)
generated_answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
if generated_answer.lower().startswith(input_text.lower()):
generated_answer = generated_answer[len(input_text):].strip()
print("Jawaban: ", generated_answer) |