Update README.md

README.md

@@ -1,101 +1,101 @@
----
-configs:
-- config_name: "passages"
-  data_files:
-  - split: train
-    path: passages_parquet/*
-- config_name: "queries"
-  data_files:
-  - split: test
-    path: queries_parquet/*
----
-
-
-# TREC-RAG 2024 Corpus (MSMARCO 2.1) - Encoded with Cohere Embed English v3
-
-
-This dataset contains the embeddings for the [TREC-RAG Corpus 2024](https://trec-rag.github.io/annoucements/2024-corpus-finalization/) embedded with the [Cohere Embed V3 English](https://cohere.com/blog/introducing-embed-v3) model.
-
-It contains embeddings for 113,520,750 passages, embeddings for 1677 queries from TREC-Deep Learning 2021-2023, as well as top-1000 hits for all queries using a brute-force (flat) index.
-
-## Search over the Index
-
-We have a pre-build index that only requires 300 MB available at [TREC-RAG-2024-index](https://huggingface.co/datasets/Cohere/trec-rag-2024-index). Just pass in your Cohere API key, and you are able to search across 113M passages.
-
-The linked index used PQ-compression with memory-mapped IVF, reducing your memory need to only 300MB, while achieving 97% search quality compared to a float32 flat index (that requires 250+GB memory and is extremely slow).
-
-
-## Passages 
-
-### Passages - Parquet
-113,520,750 passages are embedded. The parquet files can be found in the folder `passages_parquet`. Each row is a passage from the corpus. The column `emb` contains the respective embedding.
-
-You can stream the dataset for example like this:
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("Cohere/msmarco-v2.1-embed-english-v3", "passages", split="train", streaming=True)
-
-for row in dataset:
-    print(row)
-    break
-```
-
-### Passages - JSONL and Numpy
-The folder `passages_jsonl` contain the `.json.gz` files for the passages as distributed by the task organizers.
-
-The folder `passages_npy` contains a numpy matrix with all the embeddings for the respective `.json.gz` file.
-
-When your server has enough memory, you can load all doc embeddings like this:
-```python
-import numpy as np
-import glob
-
-emb_paths = sorted(glob.glob("passages_npy/*.npy"))
-
-for e_path in emb_paths:
-  doc_emb = np.load(e_path)
-```
-
-## Queries 
-
-For 1677 queries from TREC-Deep Learning 2021, 2022 and 2023 we compute the embedding and the respective top-1k hits from a brute-force (flat) index. 
-These queries can e.g. be used to test different ANN setting, e.g. in Recall@10 scenarios.
-
-We also added annotations from NIST for the 215 queries that received an annotation. These queries have a non-empty qrel column.
-
-The format is the following:
-- "_id": The query ID
-- "text": Query text
-- "trec-year": TREC-Deep Learning year 
-- "emb": Cohere Embed V3 embedding
-- "top1k_offsets": Passage ID (int) when the numpy matrices are loaded sequentially and vertically stacked
-- "top1k_passage_ids": Passage ID (string) as they appear in the dataset 
-- "top1k_cossim": Cosine similarities
-- "qrels": Relevance annotations for the 215 annotated queries by NIST. The **document relevance** scores are provided. You can get the doc_id for a passage via `row['_id'].split("#")[0]` 
-
-### Queries - JSONL
-The folder `queries_jsonl/` contains the queries in a `.jsonl.gz` format.
-
-Note: qrels are provided here as a dictionary lookup, while in the parquet format as a list in the format `[doc_id, score]` due to the limited support for dictionaries in parquet.
-
-### Queries - Parquet
-
-If you want to use the parquet file or the HF datasets library, the folder `queries_parquet/` contains the respective parquet file.
-
-You can load the queries with the following command in HF datasets
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("Cohere/msmarco-v2.1-embed-english-v3", "queries", split="test")
-
-for row in dataset:
-    print(row)
-    break
-```
-
-
-# License
-
-The embeddings are provided as Apache 2.0. The text data, qrels etc. are provided following the license of MSMARCO v2.1
+---
+configs:
+- config_name: "passages"
+  data_files:
+  - split: train
+    path: passages_parquet/*
+- config_name: "queries"
+  data_files:
+  - split: test
+    path: queries_parquet/*
+---
+
+
+# TREC-RAG 2024 Corpus (MSMARCO 2.1) - Encoded with Cohere Embed English v3
+
+
+This dataset contains the embeddings for the [TREC-RAG Corpus 2024](https://trec-rag.github.io/annoucements/2024-corpus-finalization/) embedded with the [Cohere Embed V3 English](https://cohere.com/blog/introducing-embed-v3) model.
+
+It contains embeddings for 113,520,750 passages, embeddings for 1677 queries from TREC-Deep Learning 2021-2023, as well as top-1000 hits for all queries using a brute-force (flat) index.
+
+## Search over the Index
+
+We have a pre-build index that only requires 300 MB available at [TREC-RAG-2024-index](https://huggingface.co/datasets/Cohere/trec-rag-2024-index). Just pass in your Cohere API key, and you are able to search across 113M passages.
+
+The linked index used PQ-compression with memory-mapped IVF, reducing your memory need to only 300MB, while achieving 97% search quality compared to a float32 flat index (that requires 250+GB memory and is extremely slow).
+
+
+## Passages 
+
+### Passages - Parquet
+113,520,750 passages are embedded. The parquet files can be found in the folder `passages_parquet`. Each row is a passage from the corpus. The column `emb` contains the respective embedding.
+
+You can stream the dataset for example like this:
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("CohereLabs/msmarco-v2.1-embed-english-v3", "passages", split="train", streaming=True)
+
+for row in dataset:
+    print(row)
+    break
+```
+
+### Passages - JSONL and Numpy
+The folder `passages_jsonl` contain the `.json.gz` files for the passages as distributed by the task organizers.
+
+The folder `passages_npy` contains a numpy matrix with all the embeddings for the respective `.json.gz` file.
+
+When your server has enough memory, you can load all doc embeddings like this:
+```python
+import numpy as np
+import glob
+
+emb_paths = sorted(glob.glob("passages_npy/*.npy"))
+
+for e_path in emb_paths:
+  doc_emb = np.load(e_path)
+```
+
+## Queries 
+
+For 1677 queries from TREC-Deep Learning 2021, 2022 and 2023 we compute the embedding and the respective top-1k hits from a brute-force (flat) index. 
+These queries can e.g. be used to test different ANN setting, e.g. in Recall@10 scenarios.
+
+We also added annotations from NIST for the 215 queries that received an annotation. These queries have a non-empty qrel column.
+
+The format is the following:
+- "_id": The query ID
+- "text": Query text
+- "trec-year": TREC-Deep Learning year 
+- "emb": Cohere Embed V3 embedding
+- "top1k_offsets": Passage ID (int) when the numpy matrices are loaded sequentially and vertically stacked
+- "top1k_passage_ids": Passage ID (string) as they appear in the dataset 
+- "top1k_cossim": Cosine similarities
+- "qrels": Relevance annotations for the 215 annotated queries by NIST. The **document relevance** scores are provided. You can get the doc_id for a passage via `row['_id'].split("#")[0]` 
+
+### Queries - JSONL
+The folder `queries_jsonl/` contains the queries in a `.jsonl.gz` format.
+
+Note: qrels are provided here as a dictionary lookup, while in the parquet format as a list in the format `[doc_id, score]` due to the limited support for dictionaries in parquet.
+
+### Queries - Parquet
+
+If you want to use the parquet file or the HF datasets library, the folder `queries_parquet/` contains the respective parquet file.
+
+You can load the queries with the following command in HF datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("CohereLabs/msmarco-v2.1-embed-english-v3", "queries", split="test")
+
+for row in dataset:
+    print(row)
+    break
+```
+
+
+# License
+
+The embeddings are provided as Apache 2.0. The text data, qrels etc. are provided following the license of MSMARCO v2.1