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| from transformers import T5ForConditionalGeneration, T5TokenizerFast, pipeline | |
| from transformers.models.f_t5.modeling_t5 import ( | |
| T5ForConditionalGeneration as FT5ForConditionalGeneration, | |
| ) | |
| from transformers.models.f_t5.tokenization_t5_fast import ( | |
| T5TokenizerFast as FT5TokenizerFast, | |
| ) | |
| import json | |
| with open("examples.json") as f: | |
| examples = json.load(f)["article"] | |
| model_name = "flax-community/ft5-cnn-dm" | |
| ft5_model = FT5ForConditionalGeneration.from_pretrained(model_name) | |
| ft5_tokenizer = FT5TokenizerFast.from_pretrained(model_name) | |
| ft5_summarizer = pipeline( | |
| "summarization", model=ft5_model, tokenizer=ft5_tokenizer, framework="pt" | |
| ) | |
| model_name = "flax-community/t5-base-cnn-dm" | |
| t5_model = T5ForConditionalGeneration.from_pretrained(model_name) | |
| t5_tokenizer = T5TokenizerFast.from_pretrained(model_name) | |
| t5_summarizer = pipeline( | |
| "summarization", model=t5_model, tokenizer=t5_tokenizer, framework="pt" | |
| ) | |
| def _fn(text, do_sample, min_length, max_length, temperature, top_p, summarizer): | |
| out = summarizer( | |
| text, | |
| do_sample=do_sample, | |
| min_length=min_length, | |
| max_length=max_length, | |
| temperature=temperature, | |
| top_p=top_p, | |
| truncation=True, | |
| ) | |
| return out[0]["summary_text"] | |
| def fn(*args): | |
| return [_fn(*args, summarizer=s) for s in (t5_summarizer, ft5_summarizer)] | |
| import gradio as gr | |
| interface = gr.Interface( | |
| fn, | |
| inputs=[ | |
| gr.inputs.Textbox(lines=10, label="article"), | |
| gr.inputs.Checkbox(label="do_sample", default=True), | |
| gr.inputs.Slider(1, 128, step=1, default=64, label="min_length"), | |
| gr.inputs.Slider(1, 128, step=1, default=64, label="max_length"), | |
| gr.inputs.Slider(0.0, 1.0, step=0.1, default=1, label="temperature"), | |
| gr.inputs.Slider(0.0, 1.0, step=0.1, default=1, label="top_p"), | |
| ], | |
| outputs=[ | |
| gr.outputs.Textbox(label="summary by T5"), | |
| gr.outputs.Textbox(label="summary by F-T5"), | |
| ], | |
| examples=[[ex] for ex in examples], | |
| title="F-T5 News Summarizer", | |
| description=""" | |
| F-T5 is a hybrid encoder-decoder model based on T5 and FNet. | |
| The model architecture is based on T5, except the encoder self attention is replaced by fourier transform as in FNet. | |
| The model is pre-trained on openwebtext, fine-tuned on CNN/DM. See the README for more detail. A github mirror is available here: https://github.com/cccntu/ft5-demo-space. | |
| """, | |
| ) | |
| interface.launch() | |