Update README.md

README.md

@@ -1,3 +1,148 @@
----
-license: apache-2.0
----
+---
+task_categories:
+- question-answering
+language:
+- en
+tags:
+- TREC-RAG
+- RAG
+- MSMARCO
+- MSMARCOV2.1
+- Snowflake
+- arctic
+- arctic-embed
+- arctic-embed-m-v2.0
+- 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 V2.0 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 v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) 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 | Arctic-M-V2.0 (768 Dimensions) |
+|---|---|---|
+| Deep Learning 2021 | 0.5778 | |
+| Deep Learning 2022 | 0.3576 | |
+| Deep Learning 2023 | 0.3356 ||
+| msmarcov2-dev | N/A | |
+| msmarcov2-dev2 | N/A | |
+| Raggy Queries | 0.4227 | |
+| RAG 2024 | | | 
+### Recall@100
+| Dataset | BM25 | Arctic-M-V2.0 (768 Dimensions) |
+|---|---|---|
+| Deep Learning 2021 | 0.3811 ||
+| Deep Learning 2022 | 0.233 | |
+| Deep Learning 2023 | 0.3049 | |
+| msmarcov2-dev | 0.6683 |  |
+| msmarcov2-dev2 | 0.6771 | |
+| Raggy Queries | 0.2807 |  |
+| RAG 2024 | | | 
+
+### Recall@1000
+| Dataset | BM25 | Arctic-M-V2.0 (768 Dimensions) |
+|---|---|---|
+| Deep Learning 2021 | 0.7115 |  |
+| Deep Learning 2022 | 0.479 |  |
+| Deep Learning 2023 | 0.5852 |  |
+| msmarcov2-dev | 0.8528 | |
+| msmarcov2-dev2 | 0.8577 |  |
+| Raggy Queries | 0.5745 |  |
+| RAG 2024 | | | 
+
+
+##  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-v2.0", 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-v2.0",  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-v2.0",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-v2.0')
+model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-v2.0', 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")
+```