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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) | |