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ielabgroup/
Starbucks-msmarco

Name: Starbucks-msmarco
Availability: InStock

safetensors

bert

Tevatron/msmarco-passage

2410.13230

ielabgroup/bert-base-uncased-fineweb100bt-smae

finetune

apache-2.0

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Model used in Starbucks: Improved Training for 2D Matryoshka Embeddings

This model is a bert-baase-uncased size model initialized with ielabgroup/bert-base-uncased-fineweb100bt-smae fine-tuned on MS MARCO dataset with Starbucks Representation Learning (SRL) method. SRL enables elastic layer-dimension embedding generation for search.

The following layer-dimension pairs are involved during fine-tuning: [(2, 32), (4, 64), (6, 128), (8, 256), (10, 512), (12, 768)]

To inference with all the layer-dimension pairs:

python

import torch
from transformers import AutoTokenizer, AutoModel

# trained layer-dimension sizes
sizes = [(2, 32), (4, 64), (6, 128), (8, 256), (10, 512), (12, 768)]

tokenizer = AutoTokenizer.from_pretrained("ielabgroup/Starbucks-msmarco")
model = AutoModel.from_pretrained("ielabgroup/Starbucks-msmarco").eval()

query = ["What is the capital of France?"]
passages = ["The capital of France is Paris.", "China's capital is Beijing."]


inputs = tokenizer(query, return_tensors="pt")
outputs = model(**inputs, return_dict=True, output_hidden_states=True)

# cls token embeddings of each layer-dimension size
query_embeddings = [outputs.hidden_states[layer][:, 0, :dim] for layer, dim in sizes]

passage_inputs = tokenizer(passages, return_tensors="pt", padding=True, truncation=True)
passage_outputs = model(**passage_inputs, return_dict=True, output_hidden_states=True)

passage_embeddings = [passage_outputs.hidden_states[layer][:, 0, :dim] for layer, dim in sizes]

for (layer, dim), query_embedding, passage_embedding in zip(sizes, query_embeddings, passage_embeddings):
    scores = torch.matmul(query_embedding, passage_embedding.T)
    print(f"Layer {layer}, Dimension {dim}, Scores: {scores.tolist()}")

output

Layer 2, Dimension 32, Scores: [[28.416101455688477, 18.783443450927734]]
Layer 4, Dimension 64, Scores: [[29.415122985839844, 20.81881332397461]]
Layer 6, Dimension 128, Scores: [[29.515695571899414, 20.07825469970703]]
Layer 8, Dimension 256, Scores: [[33.34524154663086, 23.34392738342285]]
Layer 10, Dimension 512, Scores: [[80.5205307006836, 63.31733322143555]]
Layer 12, Dimension 768, Scores: [[181.57217407226562, 171.1049346923828]]

To inference with target layer and dimension size (i.e., extract a small model out of it):

python

import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig

# extracted layer-dimension sizes
num_layer = 2
dim = 32

tokenizer = AutoTokenizer.from_pretrained("ielabgroup/Starbucks-msmarco")
config = AutoConfig.from_pretrained("ielabgroup/Starbucks-msmarco", num_hidden_layers=num_layer)
model = AutoModel.from_pretrained("ielabgroup/Starbucks-msmarco", config=config).eval()

print(len(model.encoder.layer)) # only has 2 layers

query = ["What is the capital of France?"]
passages = ["The capital of France is Paris.", "China's capital is Beijing."]


inputs = tokenizer(query, return_tensors="pt")
query_embeddings = model(**inputs, return_dict=True)[0][:, 0, :dim]

print(query_embeddings.shape) # torch.Size([1, 32])

passage_inputs = tokenizer(passages, return_tensors="pt", padding=True, truncation=True)
passage_embeddings = model(**passage_inputs, return_dict=True)[0][:, 0, :dim]

scores = torch.matmul(query_embeddings, passage_embeddings.T)

print(scores.tolist())

output

[[28.416101455688477, 18.783443450927734]]