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* Testing black on ui/ * Applying black on docstores * Add latest docstring and tutorial changes * Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too * Remove comments * Relax constraints on pydoc-markdown * Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade * Fix a couple of bugs * Add a type: ignore that was missing somehow * Give path to black * Apply Black * Apply Black * Relocate a couple of type: ignore * Update documentation * Make Linux CI run after applying Black * Triggering Black * Apply Black * Remove dependency, does not work well * Remove manually double trailing commas * Update documentation Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
from utils import get_document_store, index_to_doc_store, get_reader
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from haystack.document_stores.utils import eval_data_from_json
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from haystack.modeling.data_handler.processor import _download_extract_downstream_data
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from pathlib import Path
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import pandas as pd
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from results_to_json import reader as reader_json
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from templates import READER_TEMPLATE
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import json
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import logging
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logger = logging.getLogger(__name__)
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reader_models_full = [
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"deepset/roberta-base-squad2",
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"deepset/minilm-uncased-squad2",
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"deepset/bert-base-cased-squad2",
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"deepset/bert-large-uncased-whole-word-masking-squad2",
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"deepset/xlm-roberta-large-squad2",
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"distilbert-base-uncased-distilled-squad",
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]
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reader_models_ci = ["deepset/minilm-uncased-squad2"]
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reader_types = ["farm"]
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data_dir = Path("../../data/squad20")
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filename = "dev-v2.0.json"
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# Note that this number is approximate - it was calculated using Bert Base Cased
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# This number could vary when using a different tokenizer
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n_total_passages = 12350
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n_total_docs = 1204
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results_file = "reader_results.csv"
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reader_json_file = "../../docs/_src/benchmarks/reader_performance.json"
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doc_index = "eval_document"
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label_index = "label"
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def benchmark_reader(ci=False, update_json=False, save_markdown=False, **kwargs):
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if ci:
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reader_models = reader_models_ci
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else:
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reader_models = reader_models_full
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reader_results = []
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doc_store = get_document_store("elasticsearch")
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# download squad data
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_download_extract_downstream_data(input_file=data_dir / filename)
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docs, labels = eval_data_from_json(data_dir / filename, max_docs=None)
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index_to_doc_store(doc_store, docs, None, labels)
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for reader_name in reader_models:
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for reader_type in reader_types:
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logger.info(f"##### Start reader run - model:{reader_name}, type: {reader_type} ##### ")
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try:
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reader = get_reader(reader_name, reader_type)
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results = reader.eval(
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document_store=doc_store, doc_index=doc_index, label_index=label_index, device="cuda"
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)
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# print(results)
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results["passages_per_second"] = n_total_passages / results["reader_time"]
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results["reader"] = reader_name
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results["error"] = ""
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reader_results.append(results)
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except Exception as e:
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results = {
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"EM": 0.0,
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"f1": 0.0,
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"top_n_accuracy": 0.0,
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"top_n": 0,
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"reader_time": 0.0,
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"passages_per_second": 0.0,
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"seconds_per_query": 0.0,
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"reader": reader_name,
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"error": e,
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}
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reader_results.append(results)
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reader_df = pd.DataFrame.from_records(reader_results)
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reader_df.to_csv(results_file)
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if save_markdown:
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md_file = results_file.replace(".csv", ".md")
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with open(md_file, "w") as f:
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f.write(str(reader_df.to_markdown()))
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doc_store.delete_all_documents(label_index)
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doc_store.delete_all_documents(doc_index)
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if update_json:
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populate_reader_json()
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def populate_reader_json():
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reader_results = reader_json()
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template = READER_TEMPLATE
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template["data"] = reader_results
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json.dump(template, open(reader_json_file, "w"), indent=4)
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if __name__ == "__main__":
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benchmark_reader(ci=True, update_json=True, save_markdown=True)
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