File size: 1,578 Bytes
044208c |
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 |
import os
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline
model_name = "dbernsohn/roberta-java"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def preprocess_input(description):
input_text = "Generate an agent that " + description
inputs = tokenizer.encode(input_text, return_tensors='pt')
return inputs
def generate_agent_code(inputs):
generated_ids = model.generate(inputs)
agent_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
return agent_code
import os
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline
# Load the pre-trained CodeBERTa model and tokenizer
model_name = "dbernsohn/roberta-java"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Function to pre-process user input description
def preprocess_input(description):
input_text = "Generate an agent that " + description
inputs = tokenizer.encode(input_text, return_tensors='pt')
return inputs
# Function to generate agent code using the fine-tuned model
def generate_agent_code(inputs):
generated_ids = model.generate(inputs)
agent_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
return agent_code
# Example usage
user_description = "can perform sentiment analysis on text data."
inputs = preprocess_input(user_description)
generated_code = generate_agent_code(inputs)
print(generated_code)
|