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| import gradio as gr | |
| import gem_metrics | |
| from datasets import load_dataset | |
| import random | |
| random.seed(42) | |
| raw_datasets = load_dataset("eli5") | |
| validation_len = len(raw_datasets["validation_eli5"]) | |
| random_insts = [random.randint(0, validation_len) for _ in range(10)] | |
| questions = [] | |
| q2ref = {} | |
| for inst in random_insts: | |
| question = raw_datasets["validation_eli5"][inst]["title"] | |
| ref = raw_datasets["validation_eli5"][inst]["answers"]["text"] | |
| questions.append(question) | |
| q2ref[question] = ref | |
| def calc_rouge_score(og_question, pred): | |
| if pred is None: | |
| return {} | |
| preds = gem_metrics.texts.Predictions([pred]) | |
| gold = q2ref[og_question] | |
| refs = gem_metrics.texts.References([gold]) | |
| result = gem_metrics.compute(preds, refs, metrics_list=['bleu', 'rouge']) | |
| return result | |
| iface = gr.Interface( | |
| fn=calc_rouge_score, | |
| inputs=[gr.Textbox(placeholder=questions[0]), "text"], | |
| outputs=["text"], | |
| examples=[[item] for item in questions]) | |
| iface.launch() |