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import pyarrow as pa |
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import pyarrow.parquet as pq |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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|
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query_prefix = "Represent this sentence for searching relevant passages: " |
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topic_file_names = [ |
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"topics.dl21.txt", |
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"topics.dl22.txt", |
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"topics.dl23.txt", |
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"topics.msmarco-v2-doc.dev.txt", |
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"topics.msmarco-v2-doc.dev2.txt", |
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"topics.rag24.raggy-dev.txt", |
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"topics.rag24.researchy-dev.txt", |
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] |
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model_names = [ |
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"Snowflake/snowflake-arctic-embed-l", |
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"Snowflake/snowflake-arctic-embed-m-v1.5", |
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] |
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for model_name in model_names: |
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print(f"Running query embeddings using {model_name}") |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModel.from_pretrained( |
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model_name, |
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add_pooling_layer=False, |
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) |
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model.eval() |
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device = "cuda" |
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model = model.to(device) |
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for file_name in topic_file_names: |
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short_file_name = ".".join(file_name.split(".")[:-1]) |
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data = [] |
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print(f"starting on {file_name}") |
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with open(file_name, "r") as f: |
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for line in f: |
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line = line.strip().split("\t") |
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qid = line[0] |
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query_text = line[1] |
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queries_with_prefix = [ |
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"{}{}".format(query_prefix, i) for i in [query_text] |
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] |
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query_tokens = tokenizer( |
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queries_with_prefix, |
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padding=True, |
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truncation=True, |
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return_tensors="pt", |
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max_length=512, |
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) |
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|
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with torch.autocast( |
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"cuda", dtype=torch.bfloat16 |
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), torch.inference_mode(): |
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query_embeddings = ( |
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model(**query_tokens.to(device))[0][:, 0] |
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.cpu() |
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.to(torch.float32) |
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.detach() |
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.numpy()[0] |
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) |
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item = {"id": qid, "text": query_text, "embedding": query_embeddings} |
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data.append(item) |
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table = pa.Table.from_pylist(data) |
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pq.write_table( |
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table, f"{model_name.split('/')[1]}-{short_file_name}.parquet" |
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) |
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