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* Files moved, imports all broken * Fix most imports and docstrings into * Fix the paths to the modules in the API docs * Add latest docstring and tutorial changes * Add a few pipelines that were lost in the inports * Fix a bunch of mypy warnings * Add latest docstring and tutorial changes * Create a file_classifier module * Add docs for file_classifier * Fixed most circular imports, now the REST API can start * Add latest docstring and tutorial changes * Tackling more mypy issues * Reintroduce from FARM and fix last mypy issues hopefully * Re-enable old-style imports * Fix some more import from the top-level package in an attempt to sort out circular imports * Fix some imports in tests to new-style to prevent failed class equalities from breaking tests * Change document_store into document_stores * Update imports in tutorials * Add latest docstring and tutorial changes * Probably fixes summarizer tests * Improve the old-style import allowing module imports (should work) * Try to fix the docs * Remove dedicated KnowledgeGraph page from autodocs * Remove dedicated GraphRetriever page from autodocs * Fix generate_docstrings.sh with an updated list of yaml files to look for * Fix some more modules in the docs * Fix the document stores docs too * Fix a small issue on Tutorial14 * Add latest docstring and tutorial changes * Add deprecation warning to old-style imports * Remove stray folder and import Dict into dense.py * Change import path for MLFlowLogger * Add old loggers path to the import path aliases * Fix debug output of convert_ipynb.py * Fix circular import on BaseRetriever * Missed one merge block * re-run tutorial 5 * Fix imports in tutorial 5 * Re-enable squad_to_dpr CLI from the root package and move get_batches_from_generator into document_stores.base * Add latest docstring and tutorial changes * Fix typo in utils __init__ * Fix a few more imports * Fix benchmarks too * New-style imports in test_knowledge_graph * Rollback setup.py * Rollback squad_to_dpr too Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
def tutorial9_dpr_training():
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# Training Your Own "Dense Passage Retrieval" Model
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# Here are some imports that we'll need
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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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# 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/dpr_training/"
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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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doc_dir = "data/dpr_training/"
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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/ |