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| import transformers | |
| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelWithLMHead | |
| tokenizer = AutoTokenizer.from_pretrained("gpt2-large") | |
| def load_model(model_name): | |
| model = AutoModelWithLMHead.from_pretrained("gpt2-large") | |
| return model | |
| model = load_model("gpt2-large") | |
| def infer(input_ids, max_length, temperature, top_k, top_p): | |
| output_sequences = model.generate( | |
| input_ids=input_ids, | |
| max_length=max_length, | |
| temperature=temperature, | |
| top_k=top_k, | |
| top_p=top_p, | |
| do_sample=True, | |
| num_return_sequences=1 | |
| ) | |
| return output_sequences | |
| default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by the Hugging Face team, lets you write a whole document directly from your browser, and you can trigger the Transformer anywhere using the Tab key. Its like having a smart machine that completes your thoughts 😀 Get started by typing a custom snippet, check out the repository, or try one of the examples. Have fun!" | |
| #prompts | |
| st.title("Write with Transformers 🦄") | |
| st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.") | |
| sent = st.text_area("Text", default_value, height = 275) | |
| max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30) | |
| temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05) | |
| top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0) | |
| top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9) | |
| encoded_prompt = tokenizer.encode(sent, add_special_tokens=False, return_tensors="pt") | |
| if encoded_prompt.size()[-1] == 0: | |
| input_ids = None | |
| else: | |
| input_ids = encoded_prompt | |
| output_sequences = infer(input_ids, max_length, temperature, top_k, top_p) | |
| for generated_sequence_idx, generated_sequence in enumerate(output_sequences): | |
| print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===") | |
| generated_sequences = generated_sequence.tolist() | |
| # Decode text | |
| text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True) | |
| # Remove all text after the stop token | |
| #text = text[: text.find(args.stop_token) if args.stop_token else None] | |
| # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing | |
| total_sequence = ( | |
| sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :] | |
| ) | |
| generated_sequences.append(total_sequence) | |
| print(total_sequence) | |
| st.write(generated_sequences[-1]) | |