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January 1, 2023 20:51
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Encoding wikipedia sentences with sentence encoder
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| import numpy as np | |
| import os | |
| from time import perf_counter | |
| from sentence_transformers import SentenceTransformer, LoggingHandler | |
| import logging | |
| logging.basicConfig(format='%(asctime)s - %(message)s', | |
| datefmt='%Y-%m-%d %H:%M:%S', | |
| level=logging.DEBUG, | |
| handlers=[LoggingHandler()]) | |
| def encode(sentences, chunk_size=20000): | |
| print("Loaded sentences") | |
| model_mini = SentenceTransformer('all-MiniLM-L6-v2') | |
| model_mpnet = SentenceTransformer('all-mpnet-base-v2') | |
| pool = model_mpnet.start_multi_process_pool() | |
| start = perf_counter() | |
| # pool = model.start_multi_process_pool() | |
| for chunk in range(0, len(sentences), chunk_size): | |
| mini_fname = f"data/wikisent2_{chunk}.npz" | |
| mpnet_fname = f"data/wikisent2-mpnet_{chunk}.npz" | |
| begin = chunk | |
| end = chunk + chunk_size | |
| if not os.path.exists(mini_fname): | |
| print(f"Processing mini {chunk}") | |
| embeddings = model_mini.encode(sentences[begin:end], | |
| show_progress_bar=True) | |
| print(f"Encoded sentences chunk {chunk} ({begin}-{end}) - {perf_counter() - start}") | |
| np.savez(mini_fname, embeddings) | |
| print("Saved sentences") | |
| else: | |
| print(f"Skipping mini {chunk}") | |
| if not os.path.exists(mpnet_fname): | |
| print(f"Processing mpnet {chunk}") | |
| embeddings = model_mpnet.encode_multi_process(sentences[begin:end], pool) | |
| print(f"Encoded sentences chunk {chunk} ({begin}-{end}) - {perf_counter() - start}") | |
| np.savez(mpnet_fname, embeddings) | |
| print("Saved sentences") | |
| else: | |
| print(f"Skipping mpnet {chunk}") | |
| def append(encoding="mini"): | |
| # Iterate all files in data/ | |
| # Load them and append to a single file | |
| # This is to make it easier to load the data | |
| # in the future | |
| if encoding == "mini": | |
| encoding = "" | |
| files = [] | |
| # Get all wikisent2_*.npz files in a list | |
| for fname in os.listdir("data"): | |
| if encoding != "": | |
| if fname.startswith(f"wikisent2-{encoding}") and fname.endswith(".npz"): | |
| files.append(fname) | |
| # Sort by chunk number | |
| files.sort(key=lambda x: int(x.split("_")[1].split(".")[0])) | |
| # Load and append into one numpy array | |
| arrs = [] | |
| for fname in files: | |
| print(f"Loading {fname}") | |
| arrs.append(np.load(f"data/{fname}").get("arr_0")) | |
| print("Concatenating") | |
| arr = np.concatenate(arrs) | |
| print(arr.shape) | |
| np.savez("data/wikisent2_{encoding}_all.npz", arr) | |
| if __name__ == "__main__": | |
| sentences = [] | |
| with open('wikisent2.txt') as f: | |
| sentences = [line for line in f] | |
| encode(sentences) | |
| # append("mini") | |
| append("mpnet") |
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