---
task_categories:
- question-answering
language:
- en
tags:
- TREC-RAG
- RAG
- MSMARCO
- MSMARCOV2.1
- Snowflake
- gte
- stella-en-1.5B_v5
pretty_name: TREC-RAG-Embedding-Baseline stella-en-1.5B_v5
size_categories:
- 100M<n<1B
configs:
- config_name: corpus
  data_files:
  - split: train
    path: corpus/*
---

# NovaSearch stella_en_1.5B_v5 Embeddings for MSMARCO V2.1 for TREC-RAG

This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for [TREC RAG](https://trec-rag.github.io/)
All embeddings are created using [Stella EN 1.5B V5](https://huggingface.co/NovaSearch/stella_en_1.5B_v5) and are intended to serve as a simple baseline for dense retrieval-based methods.
Note, that the embeddings are not normalized so you will need to normalize them before usage.


## Retrieval Performance
Retrieval performance for the TREC DL21-23, MSMARCOV2-Dev and Raggy Queries can be found below with BM25 as a baseline. For both systems, retrieval is at the segment level and Doc Score = Max (passage score).
Retrieval is done via a dot product and happens in BF16.

NDCG @ 10
| Dataset            | BM25   | Stella-v5 |
|--------------------|--------|-----------|
| Deep Learning 2021 | 0.5778 |    0.7285 |
| Deep Learning 2022 | 0.3576 |    0.5568 |
| Deep Learning 2023 | 0.3356 |    0.4875 |
| msmarcov2-dev      | N/A    |    0.3733 |
| msmarcov2-dev2     | N/A    |    0.3778 |
| Raggy Queries      | 0.4227 |    0.5883 |
| TREC RAG (eval)    | N/A    |    0.6045 |


Recall @ 100
| Dataset            | BM25   | Stella-v5 |
|--------------------|--------|-----------|
| Deep Learning 2021 | 0.3811 |   0.42904 |
| Deep Learning 2022 |  0.233 |   0.31683 |
| Deep Learning 2023 | 0.3049 |   0.38541 |
| msmarcov2-dev      | 0.6683 |   0.88111 |
| msmarcov2-dev2     | 0.6771 |     0.871 |
| Raggy Queries      | 0.2807 |   0.36869 |
| TREC RAG (eval)    | N/A    |   0.28572 |

Recall @ 1000
| Dataset            | BM25   | Stella-v5 |
|--------------------|--------|-----------|
| Deep Learning 2021 | 0.7115 |   0.74472 |
| Deep Learning 2022 |  0.479 |   0.57709 |
| Deep Learning 2023 | 0.5852 |   0.64342 |
| msmarcov2-dev      | 0.8528 |   0.94629 |
| msmarcov2-dev2     | 0.8577 |   0.94787 |
| Raggy Queries      | 0.5745 |   0.66394 |
| TREC RAG (eval)    | N/A    |   0.68819 |

##  Loading the dataset

### Loading the document embeddings

You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset("spacemanidol/msmarco-v2.1-stella_en_1.5B_v5", split="train")
```

Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset("spacemanidol/msmarco-v2.1-stella_en_1.5B_v5",  split="train", streaming=True)
for doc in docs:
	doc_id = j['docid']
    url = doc['url']
	text = doc['text']
	emb = doc['embedding']
```


Note, The full dataset corpus is ~ 620GB so it will take a while to download and may not fit on some devices/ 

## Search
A full search example (on the first 1,000 paragraphs):
```python
from datasets import load_dataset
import torch
from transformers import AutoModel, AutoTokenizer
import numpy as np


top_k = 100
docs_stream = load_dataset("spacemanidol/msmarco-v2.1-stella_en_1.5B_v5",split="train", streaming=True)

docs = []
doc_embeddings = []

for doc in docs_stream:
    docs.append(doc)
    doc_embeddings.append(doc['embedding'])
    if len(docs) >= top_k:
        break

doc_embeddings = np.asarray(doc_embeddings)


vector_dim = 1024
vector_linear_directory = f"2_Dense_{vector_dim}"
model = AutoModel.from_pretrained('NovaSearch/stella_en_1.5B_v5', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('NovaSearch/stella_en_1.5B_v5')
vector_linear = torch.nn.Linear(in_features=model.config.hidden_size, out_features=vector_dim)
vector_linear_dict = {
    k.replace("linear.", ""): v for k, v in
    torch.load(os.path.join(model_dir, f"{vector_linear_directory}/pytorch_model.bin")).items()
}
vector_linear.load_state_dict(vector_linear_dict)
model.eval() # ensure that model and vector linear are on the same device

query_prefix = 'Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: '
queries  = ['how do you clean smoke off walls']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)

# Compute token embeddings
with torch.no_grad():
    attention_mask = *query_token["attention_mask"]
    last_hidden_state = model(***query_token)[0]
    last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
    query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
    query_vectors = normalize(vector_linear(query_vectors).cpu().numpy())


doc_embeddings = torch.nn.functional.normalize(doc_embeddings, p=2, dim=1)

# Compute dot score between query embedding and document embeddings
dot_scores = np.matmul(query_embeddings, doc_embeddings.transpose())[0]
top_k_hits = np.argpartition(dot_scores, -top_k)[-top_k:].tolist()

# Sort top_k_hits by dot score
top_k_hits.sort(key=lambda x: dot_scores[x], reverse=True)

# Print results
print("Query:", queries[0])
for doc_id in top_k_hits:
    print(docs[doc_id]['doc_id'])
    print(docs[doc_id]['text'])
    print(docs[doc_id]['url'], "\n")
```