Datasets:

lyon-nlp
/

mteb-fr-reranking-alloprof-s2p

	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/alloprof", "documents")
	# Download the queries
	queries_raw = datasets.load_dataset("lyon-nlp/alloprof", "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["text"]})
	# 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["text"])}, batched=True)

	# change document uuid with integer id
	doc_name_id_mapping = {doc["uuid"]: i for i, doc in enumerate(corpus["documents"])}
	corpus = corpus.map(lambda x: {"uuid" : doc_name_id_mapping[x["uuid"]]})
	queries = queries.map(lambda x: {"relevant": [doc_name_id_mapping[r] for r in x["relevant"]]})

	# 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_train = retrieve_documents(queries["train"]["embeddings"], corpus["documents"]["embeddings"])
	top_docs_test = retrieve_documents(queries["test"]["embeddings"], corpus["documents"]["embeddings"])
	queries["train"] = queries["train"].map(
	lambda _, i: {"top_cosim_values": top_docs_train.values[i], "top_cosim_indexes": top_docs_train.indices[i]},
	with_indices=True
	)
	queries["test"] = queries["test"].map(
	lambda _, i: {"top_cosim_values": top_docs_test.values[i], "top_cosim_indexes": top_docs_test.indices[i]},
	with_indices=True
	)

	# Remove id in best_indices if it corresponds to ground truth a relevant document
	queries = queries.map(lambda x : {"top_cosim_indexes": [i for i in x["top_cosim_indexes"] if i not in x["relevant"]]})
	# 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"][i]["text"] for i in x["relevant"]]})

	# Format as the MTEB format
	queries = queries.rename_column("text", "query")
	dataset = queries.remove_columns(['embeddings', 'relevant', 'top_cosim_values', 'top_cosim_indexes', 'answer', 'subject', "id"])
	# Rename the key of dataset key as "test"
	# dataset["test"] = dataset.pop("queries")

	# create HF repo
	repo_id = "lyon-nlp/mteb-fr-reranking-alloprof-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)