Datasets:
File size: 2,799 Bytes
537f60a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
import json
import os
import pyarrow as pa
import pyarrow.parquet as pq
import torch
from tqdm import tqdm
from transformers import AutoModel, AutoTokenizer
file_name_prefix = "msmarco_v2.1_doc_segmented_"
path = "/home/mltraining/msmarco_v2.1_doc_segmented/"
model_names = [
"Snowflake/snowflake-arctic-embed-l",
"Snowflake/snowflake-arctic-embed-m-v1.5",
]
for model_name in model_names:
print(f"Running doc embeddings using {model_name}")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(
model_name,
add_pooling_layer=False,
)
model.eval()
device = "cuda"
model = model.to(device)
dir_path = f"{path}{model_name.split('/')[1]}/"
if not os.path.exists(dir_path):
os.makedirs(dir_path)
for i in range(0, 59):
try:
filename = f"{path}{file_name_prefix}{i:02}.json"
filename_out = f"{dir_path}{i:02}.parquet"
print(f"Starting doc embeddings on {filename}")
data = []
ids = []
with open(filename, "r") as f:
for line in tqdm(f, desc="Processing JSONL file"):
j = json.loads(line)
doc_id = j["docid"]
text = j["segment"]
title = j["title"]
heading = j["headings"]
doc_text = "{} {}".format(title, text)
data.append(doc_text)
ids.append(doc_id)
print("Documents fully loaded")
batch_size = 512
chunks = [data[i: i + batch_size] for i in range(0, len(data), batch_size)]
embds = []
for chunk in tqdm(chunks, desc="inference"):
tokens = tokenizer(
chunk,
padding=True,
truncation=True,
return_tensors="pt",
max_length=512,
).to(device)
with torch.autocast(
"cuda", dtype=torch.bfloat16
), torch.inference_mode():
embds.append(
model(**tokens)[0][:, 0]
.cpu()
.to(torch.float32)
.detach()
.numpy()
)
del data, chunks
embds = [item for batch in embds for item in batch]
out_data = []
for emb, doc_id in zip(embds, ids):
out_data.append({"doc_id": doc_id, "embedding": emb})
del embds, ids
table = pa.Table.from_pylist(out_data)
del out_data
pq.write_table(table, filename_out)
except Exception:
pass
|