| import h5py as h5 | |
| import sys | |
| import json | |
| import pandas as pd | |
| import torch | |
| import time | |
| from sentence_transformers import SentenceTransformer | |
| #torch.set_num_threads(32) | |
| D = pd.read_parquet("yahoo-answers/question-answer-pair") | |
| # title, article | |
| modelname = 'sentence-transformers/all-MiniLM-L6-v2' | |
| model = SentenceTransformer(modelname) | |
| print(D.columns) | |
| #print("embeddings title") | |
| #embeddings = model.encode(D.title) | |
| # | |
| #with h5.File("ccnews.h5", "w") as f: | |
| # f["title"] = embeddings | |
| # f.attrs["model"] = modelname | |
| print("computing embeddings") | |
| st = time.time() | |
| embeddings = model.encode(D.question) | |
| print("finished in {}s".format(time.time() - st)) | |
| with h5.File("yahoo-question-answer.h5", "a") as f: | |
| f["question"] = embeddings | |
| f.attrs["model"] = modelname | |
| st = time.time() | |
| embeddings = model.encode(D.answer) | |
| print("finished in {}s".format(time.time() - st)) | |
| with h5.File("yahoo-question-answer.h5", "a") as f: | |
| f["answer"] = embeddings | |