README.md

---
task_categories:
- question-answering
language:
- en
tags:
- TREC-RAG
- RAG
- MSMARCO
- MSMARCOV2.1
- Snowflake
- arctic
- arctic-embed
- arctic-embed-v1.5
- MRL
pretty_name: TREC-RAG-Embedding-Baseline
size_categories:
- 100M<n<1B
configs:
- config_name: corpus
  data_files:
  - split: train
    path: corpus/*
---

# Snowflake Arctic Embed M V1.5 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 [Snowflake's Arctic Embed M v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) and are intended to serve as a simple baseline for dense retrieval-based methods.
It's worth noting that Snowflake's Arctic Embed M v1.5 is optimized for efficient embeddings and thus supports embedding truncation and quantization. More details on model release can be found in this [blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/) along with methods for [quantization and compression](https://github.com/Snowflake-Labs/arctic-embed/blob/main/compressed_embeddings_examples/score_arctic_embed_m_v1dot5_with_quantization.ipynb).
Note, that the embeddings are not normalized so you will need to normalize them before usage.

##  Loading the dataset

### Loading the document embeddings

You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v1.5", split="train")
```

Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v1.5",  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("Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v1.5",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)

tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m-v1.5')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-v1.5', add_pooling_layer=False)
model.eval()

query_prefix = 'Represent this sentence for searching relevant passages: '
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():
    query_embeddings = model(**query_tokens)[0][:, 0]


# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
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")
```