Upload create_data_reranking.py

create_data_reranking.py

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+import datasets
+from sentence_transformers import SentenceTransformer, util
+import torch
+from huggingface_hub import create_repo
+from huggingface_hub.utils._errors import HfHubHTTPError
+
+"""
+To create a reranking dataset from the initial retrieval dataset, 
+we use a model (sentence-transformers/all-MiniLM-L6-v2) to embed the queries and the documents.
+We then compute the cosine similarity for each query and document.
+For each query we get the topk articles, as we would for a retrieval task.
+Each couple query-document is labeled as relevant if it was labeled like so in the retrieval dataset,
+or irrelevant if it was not
+"""
+# Download the documents (corpus)
+corpus_raw = datasets.load_dataset("lyon-nlp/mteb-fr-retrieval-syntec-s2p", "documents")
+# Download the queries
+queries_raw = datasets.load_dataset("lyon-nlp/mteb-fr-retrieval-syntec-s2p", "queries")
+# Get the model
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# Generate document text (title + content)
+corpus = corpus_raw.map(lambda x: {"text": x["title"] + "\n\n" + x["content"]})
+# Embed documents and queries
+corpus = corpus.map(lambda x: {"embeddings": model.encode(x['text'])}, batched=True)
+queries = queries_raw.map(lambda x: {"embeddings": model.encode(x["Question"])}, batched=True)
+
+# change document id with index instead of name
+doc_name_id_mapping = {doc["id"]: i for i, doc in enumerate(corpus["documents"])}
+corpus = corpus.map(lambda x: {"doc_id" : doc_name_id_mapping[x["id"]]})
+queries = queries.map(lambda x: {"doc_id": doc_name_id_mapping[x["Article"]]})
+
+
+# Retrieve best documents by cosine similarity
+def retrieve_documents(queries_embs, documents_embs, topk:int=10) -> torch.return_types.topk:
+    """Finds the topk documents for each embed query among all the embed documents
+
+    Args:
+        queries_embs (_type_): the embedings of all queries of the dataset (dataset["queries"]["embeddings"])
+        documents_embs (_type_): the embedings of all coprus of the dataset (dataset["corpus"]["embeddings"])
+        topk (int, optional): The amount of top documents to retrieve. Defaults to 5.
+
+    Returns:
+        torch.return_types.topk : The topk object, with topk.values being the cosine similarities
+            and the topk.indices being the indices of best documents for each queries
+    """
+    similarities = util.cos_sim(queries_embs, documents_embs)
+    tops = torch.topk(similarities, k=topk, axis=1)
+
+    return tops
+
+top_docs = retrieve_documents(queries["queries"]["embeddings"], corpus["documents"]["embeddings"])
+queries = queries.map(
+    lambda _, i: {"top_cosim_values": top_docs.values[i], "top_cosim_indexes": top_docs.indices[i]},
+    with_indices=True
+    )
+
+# Remove id in best_indices if it corresponds to ground truth
+queries = queries.map(lambda x : {"top_cosim_indexes": [i for i in x["top_cosim_indexes"] if i != x["doc_id"]]})
+# Convert document ids to document texts based on the corpus
+queries = queries.map(lambda x: {"negative": [corpus["documents"][i]["text"] for i in x["top_cosim_indexes"]]})
+queries = queries.map(lambda x: {"positive": [corpus["documents"][x["doc_id"]]["text"]]})
+
+# Format as the MTEB format
+queries = queries.rename_column("Question", "query")
+dataset = queries.remove_columns(['Article', 'embeddings', 'doc_id', 'top_cosim_values', 'top_cosim_indexes'])
+# Rename the key of dataset key as "test"
+dataset["test"] = dataset.pop("queries")
+
+# create HF repo
+repo_id = "lyon-nlp/mteb-fr-reranking-syntec-s2p"
+try:
+    create_repo(repo_id, repo_type="dataset")
+except HfHubHTTPError as e:
+    print("HF repo already exist")
+
+# save dataset as json
+dataset.push_to_hub(repo_id)