| """ |
| import torch |
| import tensorflow as tf |
| import flax |
| import gradio as gr |
| from transformers import pipeline |
| |
| sentiment_pipeline= pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment") |
| |
| |
| # texts = ["Hugging face? weired, but memorable.", "I am despirate"] |
| |
| # results = sentiment_pipeline(texts) |
| |
| # for text, results in zip(texts, results): |
| # print(f"Text: {text}") |
| # print(f"Sentiment: {result['label']}, Score: {result['score']:.4f}\n") |
| |
| |
| def predict_sentiment(text): |
| result = sentiment_pipeline(text) |
| return result[0]['label'], result[0]['score'] |
| |
| iface = gr.Interface(fn=predict_sentiment, inputs="text", outputs = ["label","number"]) |
| |
| if __name__ == "__main__": |
| iface.launch() |
| |
| """ |
|
|
|
|
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
|
|
| torch_device = "cuda" if torch.cuda.is_available() else "cpu" |
|
|
| tokenizer = AutoTokenizer.from_pretrained("gpt2") |
|
|
| model = AutoModelForCausalLM.from_pretrained("gpt2", pad_token_id=tokenizer.eos_token_id).to(torch_device) |
|
|
| model_inputs = tokenizer('An explanation of Linear Regression: ', return_tensors='pt').to(torch_device) |
|
|
| output = model.generate(**model_inputs, max_new_tokens=50, do_sample=True, top_p=0.92, top_k=0, temperature=0.6) |
|
|
| print(tokenizer.decode(output[0],skip_special_tokens=True)) |
|
|
|
|
|
|
