--- 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("<>") # 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_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.