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import transformers | |
import streamlit as st | |
from transformers import AutoTokenizer, AutoModelWithLMHead | |
tokenizer = AutoTokenizer.from_pretrained("anonymous-german-nlp/german-gpt2") | |
def load_model(model_name): | |
model = AutoModelWithLMHead.from_pretrained("Jipski/MegStuart_gpt-2") | |
return model | |
model = load_model("Jipski/MegStuart_gpt-2") | |
def infer(input_ids, max_length, temperature, top_k, top_p, num_return_sequences): | |
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=num_return_sequences, | |
) | |
return output_sequences | |
def update_showing(): | |
st.session_state.showing = st.session_state.gen | |
default_value = "Jetzt tippen!" | |
#prompts | |
st.title("Meg Stuart gpt-2") | |
#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, key='showing', height = 275) | |
max_length = st.sidebar.slider("Max Length", min_value = 50, max_value=500) | |
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) | |
num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1) | |
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, num_return_sequences) | |
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]) |