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f9d2f786ca
* Replace FARM import statements; add dependencies * Add InferenceProc., TextCl.Proc., TextPairCl.Proc. * Remove FARMRanker, add type annotations, rename max_sample * Add sample_to_features_text for InferenceProc. * Fix type annotations: model_name_or_path is str not Path * Fix mypy errors: implement _create_dataset in TextCl.Proc. * Add task_type "embeddings" in Inferencer * Allow loading AdaptiveModel for embedding task * Add SQuAD eval metrics; enable InferenceProc for embedding task * Add baskets as param to log_samples and handle empty basket list in log_samples * Remove unused dependencies * Remove FARMClassifier (doc classificer) due to ref to TextClassificationHead * Remove FARMRanker and Classifier from doc generation scripts Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
91 lines
3.6 KiB
Python
91 lines
3.6 KiB
Python
from utils import get_document_store, index_to_doc_store, get_reader
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from haystack.preprocessor.utils import eval_data_from_json
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from haystack.modeling.data_handler.utils 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 = ["deepset/roberta-base-squad2", "deepset/minilm-uncased-squad2",
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"deepset/bert-base-cased-squad2", "deepset/bert-large-uncased-whole-word-masking-squad2",
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"deepset/xlm-roberta-large-squad2", "distilbert-base-uncased-distilled-squad"]
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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(document_store=doc_store,
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doc_index=doc_index,
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label_index=label_index,
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device="cuda")
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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 = {'EM': 0.,
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'f1': 0.,
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'top_n_accuracy': 0.,
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'top_n': 0,
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'reader_time': 0.,
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"passages_per_second": 0.,
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"seconds_per_query": 0.,
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'reader': reader_name,
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"error": e}
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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) |