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[py] test Ruri text embeddings
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| import sys | |
| args = sys.argv[1:] | |
| if len(args) != 1: | |
| print(f"Usage: {sys.argv[0]} <textfile>", file=sys.stderr) | |
| sys.exit(1) | |
| import os, torch, safetensors.torch | |
| from sentence_transformers import SentenceTransformer | |
| textfile = args[0] | |
| tensorfile = os.path.splitext(textfile)[0] + ".safetensors" | |
| # Download from the 🤗 Hub | |
| model = SentenceTransformer("cl-nagoya/ruri-base") | |
| # Don't forget to add the prefix "クエリ: " for query-side or "文章: " for passage-side texts. | |
| with open(textfile, "r") as f: | |
| lines = [l for line in f if (l := line.strip())] | |
| test = model.encode(["文章: test"], convert_to_tensor=True)[0] | |
| tensor = torch.zeros(len(lines), len(test), dtype=torch.float32) | |
| for i, line in enumerate(lines): | |
| print(f"{i+1} / {len(lines)} {line}") | |
| sentences = ["文章: " + line] | |
| tensor[i, :] = model.encode(sentences, convert_to_tensor=True)[0] | |
| safetensors.torch.save_file({"lines": tensor}, tensorfile) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import sys | |
| args = sys.argv[1:] | |
| if len(args) != 1: | |
| print(f"Usage: {sys.argv[0]} <textfile>", file=sys.stderr) | |
| sys.exit(1) | |
| import os, torch, torch.nn.functional as F, safetensors.torch | |
| from sentence_transformers import SentenceTransformer | |
| textfile = args[0] | |
| tensorfile = os.path.splitext(textfile)[0] + ".safetensors" | |
| # Download from the 🤗 Hub | |
| model = SentenceTransformer("cl-nagoya/ruri-base") | |
| with open(textfile, "r") as f: | |
| lines = [l for line in f if (l := line.strip())] | |
| tensor = safetensors.torch.load_file(tensorfile)["lines"] | |
| # Don't forget to add the prefix "クエリ: " for query-side or "文章: " for passage-side texts. | |
| while True: | |
| print() | |
| try: | |
| q = input("> ") | |
| except: | |
| print() | |
| break | |
| sentences = ["クエリ: " + q] | |
| embeddings = model.encode(sentences, convert_to_tensor=True) | |
| similarities = F.cosine_similarity(tensor, embeddings, dim=1) | |
| for i, (value, index) in enumerate(zip(*torch.topk(similarities, k=10))): | |
| v, idx = value.item(), index.item() | |
| print(f"{i+1:2d}: {v:.5f} {idx + 1:4d} {lines[idx]}") |
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