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README.md

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+---
+configs:
+- config_name: hotpotqa-corpus
+  data_files:
+  - split: train
+    path: hotpotqa/corpus/*
+- config_name: hotpotqa-queries
+  data_files:
+  - split: train
+    path: hotpotqa/queries/train.parquet
+  - split: dev
+    path: hotpotqa/queries/dev.parquet
+  - split: test
+    path: hotpotqa/queries/test.parquet
+- config_name: hotpotqa-qrels
+  data_files:
+  - split: train
+    path: hotpotqa/qrels/train.parquet
+  - split: dev
+    path: hotpotqa/qrels/dev.parquet
+  - split: test
+    path: hotpotqa/qrels/test.parquet
+- config_name: msmarco-corpus
+  data_files:
+  - split: train
+    path: msmarco/corpus/*
+- config_name: msmarco-queries
+  data_files:
+  - split: train
+    path: msmarco/queries/train.parquet
+  - split: dev
+    path: msmarco/queries/dev.parquet
+- config_name: msmarco-qrels
+  data_files:
+  - split: train
+    path: msmarco/qrels/train.parquet
+  - split: dev
+    path: msmarco/qrels/dev.parquet
+- config_name: nfcorpus-corpus
+  data_files:
+  - split: train
+    path: nfcorpus/corpus/*
+- config_name: nfcorpus-queries
+  data_files:
+  - split: train
+    path: nfcorpus/queries/train.parquet
+  - split: dev
+    path: nfcorpus/queries/dev.parquet
+  - split: test
+    path: nfcorpus/queries/test.parquet
+- config_name: nfcorpus-qrels
+  data_files:
+  - split: train
+    path: nfcorpus/qrels/train.parquet
+  - split: dev
+    path: nfcorpus/qrels/dev.parquet
+  - split: test
+    path: nfcorpus/qrels/test.parquet
+---
+
+# BEIR embeddings with Cohere embed-english-v3.0 model
+
+This datasets contains all query & document embeddings for [BEIR](https://github.com/beir-cellar/beir), embedded with the [Cohere embed-english-v3.0](https://huggingface.co/Cohere/Cohere-embed-english-v3.0) embedding model.
+
+
+## Loading the dataset
+
+### Loading the document embeddings
+The `corpus` split contains all document embeddings of the corpus.
+
+You can either load the dataset like this:
+```python
+from datasets import load_dataset
+dataset_name = "hotpotqa"
+docs = load_dataset("Cohere/beir-embed-english-v3", f"{dataset_name}-corpus", split="train")
+```
+
+Or you can also stream it without downloading it before:
+```python
+from datasets import load_dataset
+dataset_name = "hotpotqa"
+docs = load_dataset("Cohere/beir-embed-english-v3", f"{dataset_name}-corpus", split="train", streaming=True)
+for doc in docs:
+	doc_id = doc['_id']
+	title = doc['title']
+	text = doc['text']
+	emb = doc['emb']
+```
+
+Note, depending on the dataset size, the corpus split can be quite large.
+
+### Loading the query embeddings
+The `queries` split contains all query embeddings. There might be up to three splits: `train`, `dev`, and `test`, depending which splits are available in BEIR. Evaluation is performed on the `test` split.
+
+You can load the dataset like this:
+```python
+from datasets import load_dataset
+dataset_name = "hotpotqa"
+queries = load_dataset("Cohere/beir-embed-english-v3", f"{dataset_name}-queries", split="test")
+
+for query in queries:
+	query_id = query['_id']
+	text = query['text']
+	emb = query['emb']
+```
+
+
+### Loading the qrels
+
+The `qrels` split contains the query relevance annotation, i.e., it contains the relevance score for (query, document) pairs.
+
+
+You can load the dataset like this:
+```python
+from datasets import load_dataset
+dataset_name = "hotpotqa"
+qrels = load_dataset("Cohere/beir-embed-english-v3", f"{dataset_name}-qrels", split="test")
+
+for qrel in qrels:
+	query_id = qrel['query_id']
+	corpus_id = qrel['corpus_id']
+	score = qrel['score']
+```
+
+## Search
+The following shows an example, how the dataset can be used to build a semantic search application. 
+
+Get your API key from [cohere.com](https://cohere.com) and start using this dataset.
+
+```python
+#Run: pip install cohere datasets torch
+from datasets import load_dataset
+import torch
+import cohere
+dataset_name = "hotpotqa"
+co = cohere.Client("<<COHERE_API_KEY>>")  # Add your cohere API key from www.cohere.com
+
+#Load at max 1000 documents + embeddings
+max_docs = 1000
+docs_stream = load_dataset("Cohere/beir-embed-english-v3", f"{dataset_name}-corpus", split="train", streaming=True)
+docs = []
+doc_embeddings = []
+for doc in docs_stream:
+    docs.append(doc)
+    doc_embeddings.append(doc['emb'])
+    if len(docs) >= max_docs:
+        break
+
+doc_embeddings = torch.tensor(doc_embeddings)
+
+query = 'What is an abstract' #Your query 
+response = co.embed(texts=[query], model='embed-english-v3.0', input_type='search_query')
+query_embedding = response.embeddings 
+query_embedding = torch.tensor(query_embedding)
+
+# Compute dot score between query embedding and document embeddings
+dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
+top_k = torch.topk(dot_scores, k=3)
+
+# Print results
+print("Query:", query)
+for doc_id in top_k.indices[0].tolist():
+    print(docs[doc_id]['title'])
+    print(docs[doc_id]['text'], "\n")
+```
+
+
+## Running evaluations
+
+This dataset allows to reproduce the [BEIR](https://github.com/beir-cellar/beir) performance results and to compute nDCG@10, Recall@10, and Accuracy@3.
+
+You must have `beir`, `faiss`, `numpy`, and `datasets` installed. The following scripts loads all files, runs search and computes the search quality metrices.
+
+```python
+import numpy as np
+import faiss
+from beir.retrieval.evaluation import EvaluateRetrieval
+import time
+from datasets import load_dataset
+
+def faiss_search(index, queries_emb, k=[10, 100]):
+    start_time = time.time()
+    faiss_scores, faiss_doc_ids = index.search(queries_emb, max(k))
+    print(f"Search took {(time.time()-start_time):.2f} sec")
+    
+    query2id = {idx: qid for idx, qid in enumerate(query_ids)}
+    doc2id = {idx: cid for idx, cid in enumerate(docs_ids)}
+
+    faiss_results = {}
+    for idx in range(0, len(faiss_scores)):
+        qid = query2id[idx]
+        doc_scores = {doc2id[doc_id]: score.item() for doc_id, score in zip(faiss_doc_ids[idx], faiss_scores[idx])}
+        faiss_results[qid] = doc_scores
+
+    ndcg, map_score, recall, precision = EvaluateRetrieval.evaluate(qrels, faiss_results, k)
+    acc = EvaluateRetrieval.evaluate_custom(qrels, faiss_results, [3, 5, 10], metric="acc")
+    print(ndcg)
+    print(recall)
+    print(acc)
+
+dataset_name = "<<DATASET_NAME>>" 
+dataset_split = "test"
+num_dim = 1024
+
+#Load qrels
+df = load_dataset(dataset_name, "qrels", split=dataset_split)
+qrels = {}
+for row in df:
+    qid = row['query_id']
+    cid = row['corpus_id']
+    
+    if row['score'] > 0:
+        if qid not in qrels:
+            qrels[qid] = {}
+        qrels[qid][cid] = row['score']
+
+#Load queries
+df = load_dataset(dataset_name, "queries", split=dataset_split)
+
+query_ids = df['_id']
+query_embs = np.asarray(df['emb'])
+print("Query embeddings:", query_embs.shape)
+
+#Load corpus
+df = load_dataset(dataset_name, "corpus", split="train")
+
+docs_ids = df['_id']
+
+#Build index
+print("Build index. This might take some time")
+index = faiss.IndexFlatIP(num_dim)
+index.add(np.asarray(df.to_pandas()['emb'].tolist()))
+
+#Run and evaluate search
+print("Seach on index")
+faiss_search(index, query_embs)
+```
+
+## Notes
+- This dataset was created with `datasets==2.15.0`. Make sure to use this or a newer version of the datasets library.
+
+
+

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