File size: 3,517 Bytes
582a540 b492a07 582a540 d1c3444 582a540 239b2f3 582a540 239b2f3 582a540 2ffa1bd 582a540 239b2f3 582a540 ed9a903 582a540 7de082d d077743 582a540 d1c3444 7de082d 582a540 d1c3444 7de082d d077743 7de082d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
# Load pre-trained GPT-2 model and tokenizer
model_name = "gpt2-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_repeat_ngram_size=2):
"""
Generate text based on the given input text.
Parameters:
- input_text (str): The input text to start generation from.
- max_length (int): Maximum length of the generated text.
- num_beams (int): Number of beams for beam search.
- do_sample (bool): Whether to use sampling or not.
- no_repeat_ngram_size (int): Size of the n-gram to avoid repetition.
Returns:
- generated_text (str): The generated text.
"""
# Encode the input text and move it to the appropriate device
input_ids = tokenizer(input_text, return_tensors='pt')['input_ids']
# Generate text using the model
output = model.generate(input_ids, max_length=max_length, num_beams=num_beams,
do_sample=do_sample, no_repeat_ngram_size=no_repeat_ngram_size)
# Decode the generated output
generated_text = tokenizer.decode(output[0])
return generated_text
def generate_text_with_nucleus_search(input_text, max_length=16, do_sample=True, top_p=0.9):
"""
Generate text with nucleus sampling based on the given input text.
Parameters:
- input_text (str): The input text to start generation from.
- max_length (int): Maximum length of the generated text.
- do_sample (bool): Whether to use sampling or not.
- top_p (float): Nucleus sampling parameter.
Returns:
- generated_text (str): The generated text.
"""
# Encode the input text and move it to the appropriate device
input_ids = tokenizer(input_text, return_tensors='pt')['input_ids']
# Generate text using nucleus sampling
output = model.generate(input_ids, max_length=max_length, do_sample=do_sample, top_p=top_p)
# Decode the generated output
generated_text = tokenizer.decode(output[0])
return generated_text
# Create Gradio input interface
input_text_interface = gr.Textbox(lines=5, label="Input Text", placeholder="Enter text for generation...")
# Create Gradio output interface for regular text generation
output_text_interface1 = gr.Textbox(label="Generated Text (Regular)", placeholder="Generated text will appear here...")
# Interface for regular text generation
interface1 = gr.Interface(generate_text, input_text_interface, output_text_interface1,
title="Text Generation with GPT-2",
description="Generate text using the GPT-2 model with regular generation method.",
allow_flagging="never")
# Create Gradio output interface for text generation with nucleus sampling
output_text_interface2 = gr.Textbox(label="Generated Text (Nucleus Sampling)", placeholder="Generated text will appear here...")
# Interface for text generation with nucleus sampling
interface2 = gr.Interface(generate_text_with_nucleus_search, input_text_interface, output_text_interface2,
title="Text Generation with Nucleus Sampling",
description="Generate text using nucleus sampling with the GPT-2 model.",
allow_flagging="never")
# Launch both interfaces
interface1.launch(share=True)
interface2.launch(share=True)
|