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* move logging config from haystack lib to application * Update Documentation & Code Style * config logging before importing haystack * Update Documentation & Code Style * add logging config to all tutorials * Update Documentation & Code Style Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
# Training Your Own "Dense Passage Retrieval" Model
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# Here are some imports that we'll need
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import logging
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# We configure how logging messages should be displayed and which log level should be used before importing Haystack.
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# Example log message:
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# INFO - haystack.utils.preprocessing - Converting data/tutorial1/218_Olenna_Tyrell.txt
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# Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
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logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING)
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logging.getLogger("haystack").setLevel(logging.INFO)
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from haystack.nodes import DensePassageRetriever
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from haystack.utils import fetch_archive_from_http
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from haystack.document_stores import InMemoryDocumentStore
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def tutorial9_dpr_training():
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# Download original DPR data
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# WARNING: the train set is 7.4GB and the dev set is 800MB
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doc_dir = "data/tutorial9"
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s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
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s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
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fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "/train")
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fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "/dev")
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## Option 1: Training DPR from Scratch
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# Here are the variables to specify our training data, the models that we use to initialize DPR
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# and the directory where we'll be saving the model
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train_filename = "train/biencoder-nq-train.json"
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dev_filename = "dev/biencoder-nq-dev.json"
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query_model = "bert-base-uncased"
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passage_model = "bert-base-uncased"
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save_dir = "../saved_models/dpr"
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# ## Option 2: Finetuning DPR
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#
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# # Here are the variables you might want to use instead of the set above
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# # in order to perform pretraining
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#
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# doc_dir = "PATH_TO_YOUR_DATA_DIR"
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# train_filename = "TRAIN_FILENAME"
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# dev_filename = "DEV_FILENAME"
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#
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# query_model = "facebook/dpr-question_encoder-single-nq-base"
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# passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
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#
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# save_dir = "..saved_models/dpr"
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## Initialize DPR model
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retriever = DensePassageRetriever(
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document_store=InMemoryDocumentStore(),
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query_embedding_model=query_model,
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passage_embedding_model=passage_model,
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max_seq_len_query=64,
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max_seq_len_passage=256,
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)
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# Start training our model and save it when it is finished
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retriever.train(
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data_dir=doc_dir,
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train_filename=train_filename,
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dev_filename=dev_filename,
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test_filename=dev_filename,
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n_epochs=1,
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batch_size=16,
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grad_acc_steps=8,
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save_dir=save_dir,
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evaluate_every=3000,
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embed_title=True,
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num_positives=1,
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num_hard_negatives=1,
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)
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## Loading
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reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
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if __name__ == "__main__":
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tutorial9_dpr_training()
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# This Haystack script was made with love by deepset in Berlin, Germany
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# Haystack: https://github.com/deepset-ai/haystack
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# deepset: https://deepset.ai/
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