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import gradio as gr
import transformers
from transformers import pipeline
from transformers import BloomTokenizerFast
def generate(checkpoint, input_prompt):
tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom")
generator = pipeline("text-generation", model='simonosgoode/bloom-560m-finetuned-cdn_law', tokenizer=tokenizer)
generated_judgement = generator(input_prompt
, max_length = 100
, num_return_sequences = 1
, return_full_text = True
, verbose = 0
#, num_beams = 1
#, early_stopping = True
, temperature = 0.7
#, top_k = 50 # Default 50
, top_p = 1 # Default 1.0
, no_repeat_ngram_size = 3 # Default = 0
, repetition_penalty = 1.0 # Default = 1.0
#, do_sample = True # Default = False
)[0]["generated_text"]
return generated_judgement
with gr.Blocks() as judgements:
inputs = gr.Textbox(lines=10, label="Input paragraph")
output = gr.Textbox(lines=10, label="Output paragraph")
btn = gr.Button("Generate the next paragraph of a judgement")
btn.click(fn=generate, inputs=inputs, outputs=output)
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