e5-large-v2-4096 (LSG-Patched)

A Local-Sparse-Global (LSG) version of intfloat/e5-large-v2, capable of handling up to 4096 tokens. This model has been patched to work with more recent versions of Transformers—ensuring is_decoder=False for pure encoder mode and carefully handling attention masks at float precision.

Quickstart Usage

Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.

import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoConfig, AutoModel

def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
    """
    E5-style average pooling:
    1) Zero out masked tokens
    2) Sum the token embeddings
    3) Divide by the number of valid tokens
    """
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    summed = last_hidden.sum(dim=1)
    counts = attention_mask.sum(dim=1)
    return summed / counts.unsqueeze(1)

model_name = "guymorganb/e5-large-v2-4096-lsg-patched"

# 1) Load config (force encoder mode)
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
config.is_decoder = False

# 2) Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, config=config, trust_remote_code=True)

# 3) Some example queries & passages
input_texts = [
  'query: how much protein should a female eat',
  'query: summit define',
  "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
  "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
]

# 4) Tokenize
batch_dict = tokenizer(
    input_texts,
    max_length=4096,
    padding=True,
    truncation=True,
    return_tensors="pt"
)

# Convert attention mask to float (needed for LSG code)
batch_dict["attention_mask"] = batch_dict["attention_mask"].float()

# 5) Forward pass (return_dict=True gives a dictionary output)
outputs = model(**batch_dict, return_dict=True)

# 6) Pool
embeddings = average_pool(outputs.last_hidden_state, batch_dict["attention_mask"])

# 7) Normalize (optional)
embeddings = F.normalize(embeddings, p=2, dim=1)

# 8) Example similarity: compare first two (queries) vs. last two (passages)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print("Similarity scores:\n", scores.tolist())

or...test for inference

# Modified test script
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoConfig, AutoModel
import time

# Keep your average_pool function the same

model_name = "guymorganb/e5-large-v2-4096-lsg-patched"

# Load with explicit LSG settings
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
config.is_decoder = False
config.block_size = 4096          # Double the block size #############
config.sparse_block_size = 4096   # Keep equal to block_size ##############
config.sparsity_factor = 2        
config.sparsity_type = "norm"
config.adaptive = True
config.num_global_tokens = 1     
config.pool_with_global = True

print("Config after loading:")
for k, v in config.to_dict().items():
    print(f"{k}: {v}")

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(
    model_name,
    config=config,
    trust_remote_code=True
)
model.eval()

# Test with gradually increasing lengths
test_lengths = [
    10,     # Very short
    64,     # 
    128,    # 
    256,    # 
    512,    # 
    1024,   # 
    2048,   # 
    3072,   # 
    4096    # Full context
]

for length in test_lengths:
    test_text = f"passage: {'test ' * length }"
    
    try:
        encoding = tokenizer(
            test_text,
            max_length=4096,
            padding=True,
            # pad_to_multiple_of=4096,  # dont use unless you want a fixed size
            truncation=True,
            return_tensors='pt'
        )
        
        actual_length = encoding['input_ids'].size(1)
        print(f"\nTesting length {actual_length} tokens:")
        print(f"Input tensor shape: {encoding['input_ids'].shape}")
        
        start = time.time()
        with torch.no_grad():
            encoding["attention_mask"] = encoding["attention_mask"].float()
            outputs = model(**encoding, return_dict=True)
            embeddings = average_pool(outputs.last_hidden_state, encoding["attention_mask"])
            embeddings = F.normalize(embeddings, p=2, dim=1)
        
        end = time.time()
        print(f"Success! Processing time: {end - start:.3f} seconds")
        print(f"Embedding shape: {embeddings.shape}")
        
    except RuntimeError as e:
        print(f"Failed at length {actual_length}")
        print(f"Error: {str(e)}")
        print(f"Last successful shape: {encoding['input_ids'].shape}")
        break

@article{wang2022text,
  title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
  journal={arXiv preprint arXiv:2212.03533},
  year={2022}
}

@misc{guymorganb_e5_lsg_patch,
  title={{LSG Patch for E5-large-v2 up to 4096 tokens}},
  author={{GuyMorganB}},
  howpublished={\url{https://huggingface.co/guymorganb/e5-large-v2-4096-lsg-patched}},
  year={2023}
}