[Multi-vector]Add timing instrumentation and multi-dataset support for multi-vector… (#161)
* Add timing instrumentation and multi-dataset support for multi-vector retrieval - Add timing measurements for search operations (load and core time) - Increase embedding batch size from 1 to 32 for better performance - Add explicit memory cleanup with del all_embeddings - Support loading and merging multiple datasets with different splits - Add CLI arguments for search method selection (ann/exact/exact-all) - Auto-detect image field names across different dataset structures - Print candidate doc counts for performance monitoring 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * update vidore * reproduce docvqa results * reproduce docvqa results and add debug file --------- Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Yichuan Wang
@@ -3,6 +3,7 @@ import json
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import os
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import re
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import sys
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import time
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from pathlib import Path
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from typing import Any, Optional, cast
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@@ -194,7 +195,7 @@ def _embed_images(model, processor, images: list[Image.Image]) -> list[Any]:
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dataloader = DataLoader(
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dataset=ListDataset[Image.Image](images),
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batch_size=1,
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batch_size=32,
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shuffle=False,
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collate_fn=lambda x: processor.process_images(x),
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)
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@@ -218,32 +219,47 @@ def _embed_images(model, processor, images: list[Image.Image]) -> list[Any]:
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def _embed_queries(model, processor, queries: list[str]) -> list[Any]:
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import torch
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from colpali_engine.utils.torch_utils import ListDataset
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from torch.utils.data import DataLoader
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model.eval()
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dataloader = DataLoader(
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dataset=ListDataset[str](queries),
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batch_size=1,
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shuffle=False,
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collate_fn=lambda x: processor.process_queries(x),
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)
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# Match MTEB's exact query processing from ColPaliEngineWrapper.get_text_embeddings:
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# 1. MTEB receives batch["text"] which already includes instruction/prompt (from _combine_queries_with_instruction_text)
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# 2. Manually adds: query_prefix + text + query_augmentation_token * 10
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# 3. Calls processor.process_queries(batch) where batch is now a list of strings
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# 4. process_queries adds: query_prefix + text + suffix (suffix = query_augmentation_token * 10)
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#
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# This results in duplicate addition: query_prefix is added twice, query_augmentation_token * 20 total
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# We need to match this exactly to reproduce MTEB results
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all_embeds = []
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batch_size = 32 # Match MTEB's default batch_size
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with torch.no_grad():
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for i in tqdm(range(0, len(queries), batch_size), desc="Embedding queries"):
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batch_queries = queries[i : i + batch_size]
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# Match MTEB: manually add query_prefix + text + query_augmentation_token * 10
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# Then process_queries will add them again (resulting in 20 augmentation tokens total)
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batch = [
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processor.query_prefix + t + processor.query_augmentation_token * 10
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for t in batch_queries
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]
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inputs = processor.process_queries(batch)
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inputs = {k: v.to(model.device) for k, v in inputs.items()}
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q_vecs: list[Any] = []
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for batch_query in tqdm(dataloader, desc="Embedding queries"):
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with torch.no_grad():
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batch_query = {k: v.to(model.device) for k, v in batch_query.items()}
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if model.device.type == "cuda":
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with torch.autocast(
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device_type="cuda",
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dtype=model.dtype if model.dtype.is_floating_point else torch.bfloat16,
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):
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embeddings_query = model(**batch_query)
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outs = model(**inputs)
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else:
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embeddings_query = model(**batch_query)
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q_vecs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
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return q_vecs
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outs = model(**inputs)
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# Match MTEB: convert to float32 on CPU
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all_embeds.extend(list(torch.unbind(outs.cpu().to(torch.float32))))
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return all_embeds
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def _build_index(
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@@ -283,6 +299,279 @@ def _load_retriever_if_index_exists(index_path: str) -> Optional[Any]:
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return None
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def _build_fast_plaid_index(
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index_path: str,
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doc_vecs: list[Any],
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filepaths: list[str],
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images: list[Image.Image],
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) -> tuple[Any, float]:
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"""
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Build a Fast-Plaid index from document embeddings.
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Args:
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index_path: Path to save the Fast-Plaid index
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doc_vecs: List of document embeddings (each is a tensor with shape [num_tokens, embedding_dim])
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filepaths: List of filepath identifiers for each document
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images: List of PIL Images corresponding to each document
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Returns:
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Tuple of (FastPlaid index object, build_time_in_seconds)
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"""
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import torch
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from fast_plaid import search as fast_plaid_search
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print(f" Preparing {len(doc_vecs)} document embeddings for Fast-Plaid...")
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_t0 = time.perf_counter()
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# Convert doc_vecs to list of tensors
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documents_embeddings = []
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for i, vec in enumerate(doc_vecs):
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if i % 1000 == 0:
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print(f" Converting embedding {i}/{len(doc_vecs)}...")
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if not isinstance(vec, torch.Tensor):
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vec = (
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torch.tensor(vec)
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if isinstance(vec, np.ndarray)
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else torch.from_numpy(np.array(vec))
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)
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# Ensure float32 for Fast-Plaid
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if vec.dtype != torch.float32:
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vec = vec.float()
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documents_embeddings.append(vec)
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print(f" Converted {len(documents_embeddings)} embeddings")
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if len(documents_embeddings) > 0:
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print(f" First embedding shape: {documents_embeddings[0].shape}")
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print(f" First embedding dtype: {documents_embeddings[0].dtype}")
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# Prepare metadata for Fast-Plaid
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print(f" Preparing metadata for {len(filepaths)} documents...")
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metadata_list = []
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for i, filepath in enumerate(filepaths):
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metadata_list.append(
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{
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"filepath": filepath,
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"index": i,
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}
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)
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# Create Fast-Plaid index
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print(f" Creating FastPlaid object with index path: {index_path}")
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try:
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fast_plaid_index = fast_plaid_search.FastPlaid(index=index_path)
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print(" FastPlaid object created successfully")
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except Exception as e:
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print(f" Error creating FastPlaid object: {type(e).__name__}: {e}")
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import traceback
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traceback.print_exc()
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raise
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print(f" Calling fast_plaid_index.create() with {len(documents_embeddings)} documents...")
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try:
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fast_plaid_index.create(
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documents_embeddings=documents_embeddings,
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metadata=metadata_list,
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)
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print(" Fast-Plaid index created successfully")
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except Exception as e:
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print(f" Error creating Fast-Plaid index: {type(e).__name__}: {e}")
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import traceback
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traceback.print_exc()
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raise
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build_secs = time.perf_counter() - _t0
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# Save images separately (Fast-Plaid doesn't store images)
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print(f" Saving {len(images)} images...")
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images_dir = Path(index_path) / "images"
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images_dir.mkdir(parents=True, exist_ok=True)
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for i, img in enumerate(tqdm(images, desc="Saving images")):
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img_path = images_dir / f"doc_{i}.png"
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img.save(str(img_path))
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return fast_plaid_index, build_secs
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def _fast_plaid_index_exists(index_path: str) -> bool:
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"""
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Check if Fast-Plaid index exists by checking for key files.
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This avoids creating the FastPlaid object which may trigger memory allocation.
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Args:
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index_path: Path to the Fast-Plaid index
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Returns:
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True if index appears to exist, False otherwise
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"""
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index_path_obj = Path(index_path)
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if not index_path_obj.exists() or not index_path_obj.is_dir():
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return False
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# Fast-Plaid creates a SQLite database file for metadata
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# Check for metadata.db as the most reliable indicator
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metadata_db = index_path_obj / "metadata.db"
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if metadata_db.exists() and metadata_db.stat().st_size > 0:
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return True
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# Also check if directory has any files (might be incomplete index)
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try:
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if any(index_path_obj.iterdir()):
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return True
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except Exception:
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pass
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return False
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def _load_fast_plaid_index_if_exists(index_path: str) -> Optional[Any]:
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"""
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Load Fast-Plaid index if it exists.
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First checks if index files exist, then creates the FastPlaid object.
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The actual index data loading happens lazily when search is called.
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Args:
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index_path: Path to the Fast-Plaid index
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Returns:
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FastPlaid index object if exists, None otherwise
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"""
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try:
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from fast_plaid import search as fast_plaid_search
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# First check if index files exist without creating the object
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if not _fast_plaid_index_exists(index_path):
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return None
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# Now try to create FastPlaid object
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# This may trigger some memory allocation, but the full index loading is deferred
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fast_plaid_index = fast_plaid_search.FastPlaid(index=index_path)
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return fast_plaid_index
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except ImportError:
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# fast-plaid not installed
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return None
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except Exception as e:
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# Any error (including memory errors from Rust backend) - return None
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# The error will be caught and index will be rebuilt
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print(f"Warning: Could not load Fast-Plaid index: {type(e).__name__}: {e}")
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return None
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def _search_fast_plaid(
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fast_plaid_index: Any,
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query_vec: Any,
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top_k: int,
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) -> tuple[list[tuple[float, int]], float]:
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"""
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Search Fast-Plaid index with a query embedding.
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Args:
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fast_plaid_index: FastPlaid index object
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query_vec: Query embedding tensor with shape [num_tokens, embedding_dim]
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top_k: Number of top results to return
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Returns:
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Tuple of (results_list, search_time_in_seconds)
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results_list: List of (score, doc_id) tuples
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"""
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import torch
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_t0 = time.perf_counter()
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# Ensure query is a torch tensor
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if not isinstance(query_vec, torch.Tensor):
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q_vec_tensor = (
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torch.tensor(query_vec)
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if isinstance(query_vec, np.ndarray)
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else torch.from_numpy(np.array(query_vec))
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)
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else:
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q_vec_tensor = query_vec
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# Fast-Plaid expects shape [num_queries, num_tokens, embedding_dim]
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if q_vec_tensor.dim() == 2:
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q_vec_tensor = q_vec_tensor.unsqueeze(0) # [1, num_tokens, embedding_dim]
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# Perform search
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scores = fast_plaid_index.search(
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queries_embeddings=q_vec_tensor,
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top_k=top_k,
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show_progress=True,
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)
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search_secs = time.perf_counter() - _t0
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# Convert Fast-Plaid results to same format as LEANN: list of (score, doc_id) tuples
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results = []
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if scores and len(scores) > 0:
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query_results = scores[0]
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# Fast-Plaid returns (doc_id, score), convert to (score, doc_id) to match LEANN format
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results = [(float(score), int(doc_id)) for doc_id, score in query_results]
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return results, search_secs
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def _get_fast_plaid_image(index_path: str, doc_id: int) -> Optional[Image.Image]:
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"""
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Retrieve image for a document from Fast-Plaid index.
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Args:
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index_path: Path to the Fast-Plaid index
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doc_id: Document ID returned by Fast-Plaid search
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Returns:
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PIL Image if found, None otherwise
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Note: Uses metadata['index'] to get the actual file index, as Fast-Plaid
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doc_id may differ from the file naming index.
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"""
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# First get metadata to find the actual index used for file naming
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metadata = _get_fast_plaid_metadata(index_path, doc_id)
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if metadata is None:
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# Fallback: try using doc_id directly
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file_index = doc_id
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else:
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# Use the 'index' field from metadata, which matches the file naming
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file_index = metadata.get("index", doc_id)
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images_dir = Path(index_path) / "images"
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image_path = images_dir / f"doc_{file_index}.png"
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if image_path.exists():
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return Image.open(image_path)
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# If not found with index, try doc_id as fallback
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if file_index != doc_id:
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fallback_path = images_dir / f"doc_{doc_id}.png"
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if fallback_path.exists():
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return Image.open(fallback_path)
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return None
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def _get_fast_plaid_metadata(index_path: str, doc_id: int) -> Optional[dict]:
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"""
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Retrieve metadata for a document from Fast-Plaid index.
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Args:
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index_path: Path to the Fast-Plaid index
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doc_id: Document ID
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Returns:
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Dictionary with metadata if found, None otherwise
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"""
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try:
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from fast_plaid import filtering
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metadata_list = filtering.get(index=index_path, subset=[doc_id])
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if metadata_list and len(metadata_list) > 0:
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return metadata_list[0]
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except Exception:
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pass
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return None
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def _generate_similarity_map(
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model,
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processor,
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@@ -678,11 +967,15 @@ class LeannMultiVector:
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return (float(score), doc_id)
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scores: list[tuple[float, int]] = []
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# load and core time
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start_time = time.time()
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as ex:
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futures = [ex.submit(_score_one, doc_id) for doc_id in candidate_doc_ids]
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for fut in concurrent.futures.as_completed(futures):
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scores.append(fut.result())
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end_time = time.time()
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print(f"Number of candidate doc ids: {len(candidate_doc_ids)}")
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print(f"Time taken in load and core time: {end_time - start_time} seconds")
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scores.sort(key=lambda x: x[0], reverse=True)
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return scores[:topk] if len(scores) >= topk else scores
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@@ -710,7 +1003,6 @@ class LeannMultiVector:
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emb_path = self._embeddings_path()
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if not emb_path.exists():
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return self.search(data, topk)
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all_embeddings = np.load(emb_path, mmap_mode="r")
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if all_embeddings.dtype != np.float32:
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all_embeddings = all_embeddings.astype(np.float32)
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@@ -718,23 +1010,29 @@ class LeannMultiVector:
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assert self._docid_to_indices is not None
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candidate_doc_ids = list(self._docid_to_indices.keys())
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def _score_one(doc_id: int) -> tuple[float, int]:
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def _score_one(doc_id: int, _all_embeddings=all_embeddings) -> tuple[float, int]:
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token_indices = self._docid_to_indices.get(doc_id, [])
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if not token_indices:
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return (0.0, doc_id)
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doc_vecs = np.asarray(all_embeddings[token_indices], dtype=np.float32)
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doc_vecs = np.asarray(_all_embeddings[token_indices], dtype=np.float32)
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sim = np.dot(data, doc_vecs.T)
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sim = np.nan_to_num(sim, nan=-1e30, posinf=1e30, neginf=-1e30)
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score = sim.max(axis=2).sum(axis=1) if sim.ndim == 3 else sim.max(axis=1).sum()
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return (float(score), doc_id)
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scores: list[tuple[float, int]] = []
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# load and core time
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start_time = time.time()
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as ex:
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futures = [ex.submit(_score_one, d) for d in candidate_doc_ids]
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for fut in concurrent.futures.as_completed(futures):
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scores.append(fut.result())
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end_time = time.time()
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# print number of candidate doc ids
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print(f"Number of candidate doc ids: {len(candidate_doc_ids)}")
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print(f"Time taken in load and core time: {end_time - start_time} seconds")
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scores.sort(key=lambda x: x[0], reverse=True)
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del all_embeddings
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return scores[:topk] if len(scores) >= topk else scores
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def get_image(self, doc_id: int) -> Optional[Image.Image]:
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@@ -778,3 +1076,259 @@ class LeannMultiVector:
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"image_path": meta.get("image_path", ""),
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}
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return None
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class ViDoReBenchmarkEvaluator:
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"""
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A reusable class for evaluating ViDoRe benchmarks (v1 and v2).
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This class encapsulates common functionality for building indexes, searching, and evaluating.
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"""
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def __init__(
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self,
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model_name: str,
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use_fast_plaid: bool = False,
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top_k: int = 100,
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first_stage_k: int = 500,
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k_values: Optional[list[int]] = None,
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):
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"""
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Initialize the evaluator.
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Args:
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model_name: Model name ("colqwen2" or "colpali")
|
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use_fast_plaid: Whether to use Fast-Plaid instead of LEANN
|
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top_k: Top-k results to retrieve
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first_stage_k: First stage k for LEANN search
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k_values: List of k values for evaluation metrics
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"""
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self.model_name = model_name
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self.use_fast_plaid = use_fast_plaid
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||||
self.top_k = top_k
|
||||
self.first_stage_k = first_stage_k
|
||||
self.k_values = k_values if k_values is not None else [1, 3, 5, 10, 100]
|
||||
|
||||
# Load model once (can be reused across tasks)
|
||||
self._model = None
|
||||
self._processor = None
|
||||
self._model_name_actual = None
|
||||
|
||||
def _load_model_if_needed(self):
|
||||
"""Lazy load the model."""
|
||||
if self._model is None:
|
||||
print(f"\nLoading model: {self.model_name}")
|
||||
self._model_name_actual, self._model, self._processor, _, _, _ = _load_colvision(
|
||||
self.model_name
|
||||
)
|
||||
print(f"Model loaded: {self._model_name_actual}")
|
||||
|
||||
def build_index_from_corpus(
|
||||
self,
|
||||
corpus: dict[str, Image.Image],
|
||||
index_path: str,
|
||||
rebuild: bool = False,
|
||||
) -> tuple[Any, list[str]]:
|
||||
"""
|
||||
Build index from corpus images.
|
||||
|
||||
Args:
|
||||
corpus: dict mapping corpus_id to PIL Image
|
||||
index_path: Path to save/load the index
|
||||
rebuild: Whether to rebuild even if index exists
|
||||
|
||||
Returns:
|
||||
tuple: (retriever or fast_plaid_index object, list of corpus_ids in order)
|
||||
"""
|
||||
self._load_model_if_needed()
|
||||
|
||||
# Ensure consistent ordering
|
||||
corpus_ids = sorted(corpus.keys())
|
||||
images = [corpus[cid] for cid in corpus_ids]
|
||||
|
||||
if self.use_fast_plaid:
|
||||
# Check if Fast-Plaid index exists
|
||||
if not rebuild and _load_fast_plaid_index_if_exists(index_path) is not None:
|
||||
print(f"Fast-Plaid index already exists at {index_path}")
|
||||
return _load_fast_plaid_index_if_exists(index_path), corpus_ids
|
||||
|
||||
print(f"Building Fast-Plaid index at {index_path}...")
|
||||
print("Embedding images...")
|
||||
doc_vecs = _embed_images(self._model, self._processor, images)
|
||||
|
||||
fast_plaid_index, build_time = _build_fast_plaid_index(
|
||||
index_path, doc_vecs, corpus_ids, images
|
||||
)
|
||||
print(f"Fast-Plaid index built in {build_time:.2f}s")
|
||||
return fast_plaid_index, corpus_ids
|
||||
else:
|
||||
# Check if LEANN index exists
|
||||
if not rebuild:
|
||||
retriever = _load_retriever_if_index_exists(index_path)
|
||||
if retriever is not None:
|
||||
print(f"LEANN index already exists at {index_path}")
|
||||
return retriever, corpus_ids
|
||||
|
||||
print(f"Building LEANN index at {index_path}...")
|
||||
print("Embedding images...")
|
||||
doc_vecs = _embed_images(self._model, self._processor, images)
|
||||
|
||||
retriever = _build_index(index_path, doc_vecs, corpus_ids, images)
|
||||
print("LEANN index built")
|
||||
return retriever, corpus_ids
|
||||
|
||||
def search_queries(
|
||||
self,
|
||||
queries: dict[str, str],
|
||||
corpus_ids: list[str],
|
||||
index_or_retriever: Any,
|
||||
fast_plaid_index_path: Optional[str] = None,
|
||||
task_prompt: Optional[dict[str, str]] = None,
|
||||
) -> dict[str, dict[str, float]]:
|
||||
"""
|
||||
Search queries against the index.
|
||||
|
||||
Args:
|
||||
queries: dict mapping query_id to query text
|
||||
corpus_ids: list of corpus_ids in the same order as the index
|
||||
index_or_retriever: index or retriever object
|
||||
fast_plaid_index_path: path to Fast-Plaid index (for metadata)
|
||||
task_prompt: Optional dict with prompt for query (e.g., {"query": "..."})
|
||||
|
||||
Returns:
|
||||
results: dict mapping query_id to dict of {corpus_id: score}
|
||||
"""
|
||||
self._load_model_if_needed()
|
||||
|
||||
print(f"Searching {len(queries)} queries (top_k={self.top_k})...")
|
||||
|
||||
query_ids = list(queries.keys())
|
||||
query_texts = [queries[qid] for qid in query_ids]
|
||||
|
||||
# Note: ColPaliEngineWrapper does NOT use task prompt from metadata
|
||||
# It uses query_prefix + text + query_augmentation_token (handled in _embed_queries)
|
||||
# So we don't append task_prompt here to match MTEB behavior
|
||||
|
||||
# Embed queries
|
||||
print("Embedding queries...")
|
||||
query_vecs = _embed_queries(self._model, self._processor, query_texts)
|
||||
|
||||
results = {}
|
||||
|
||||
for query_id, query_vec in zip(tqdm(query_ids, desc="Searching"), query_vecs):
|
||||
if self.use_fast_plaid:
|
||||
# Fast-Plaid search
|
||||
search_results, _ = _search_fast_plaid(index_or_retriever, query_vec, self.top_k)
|
||||
query_results = {}
|
||||
for score, doc_id in search_results:
|
||||
if doc_id < len(corpus_ids):
|
||||
corpus_id = corpus_ids[doc_id]
|
||||
query_results[corpus_id] = float(score)
|
||||
else:
|
||||
# LEANN search
|
||||
import torch
|
||||
|
||||
query_np = (
|
||||
query_vec.float().numpy() if isinstance(query_vec, torch.Tensor) else query_vec
|
||||
)
|
||||
search_results = index_or_retriever.search_exact(query_np, topk=self.top_k)
|
||||
query_results = {}
|
||||
for score, doc_id in search_results:
|
||||
if doc_id < len(corpus_ids):
|
||||
corpus_id = corpus_ids[doc_id]
|
||||
query_results[corpus_id] = float(score)
|
||||
|
||||
results[query_id] = query_results
|
||||
|
||||
return results
|
||||
|
||||
@staticmethod
|
||||
def evaluate_results(
|
||||
results: dict[str, dict[str, float]],
|
||||
qrels: dict[str, dict[str, int]],
|
||||
k_values: Optional[list[int]] = None,
|
||||
) -> dict[str, float]:
|
||||
"""
|
||||
Evaluate retrieval results using NDCG and other metrics.
|
||||
|
||||
Args:
|
||||
results: dict mapping query_id to dict of {corpus_id: score}
|
||||
qrels: dict mapping query_id to dict of {corpus_id: relevance_score}
|
||||
k_values: List of k values for evaluation metrics
|
||||
|
||||
Returns:
|
||||
Dictionary of metric scores
|
||||
"""
|
||||
try:
|
||||
from mteb._evaluators.retrieval_metrics import (
|
||||
calculate_retrieval_scores,
|
||||
make_score_dict,
|
||||
)
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"pytrec_eval is required for evaluation. Install with: pip install pytrec-eval"
|
||||
)
|
||||
|
||||
if k_values is None:
|
||||
k_values = [1, 3, 5, 10, 100]
|
||||
|
||||
# Check if we have any queries to evaluate
|
||||
if len(results) == 0:
|
||||
print("Warning: No queries to evaluate. Returning zero scores.")
|
||||
scores = {}
|
||||
for k in k_values:
|
||||
scores[f"ndcg_at_{k}"] = 0.0
|
||||
scores[f"map_at_{k}"] = 0.0
|
||||
scores[f"recall_at_{k}"] = 0.0
|
||||
scores[f"precision_at_{k}"] = 0.0
|
||||
scores[f"mrr_at_{k}"] = 0.0
|
||||
return scores
|
||||
|
||||
print(f"Evaluating results with k_values={k_values}...")
|
||||
print(f"Before filtering: {len(results)} results, {len(qrels)} qrels")
|
||||
|
||||
# Filter to ensure qrels and results have the same query set
|
||||
# This matches MTEB behavior: only evaluate queries that exist in both
|
||||
# pytrec_eval only evaluates queries in qrels, so we need to ensure
|
||||
# results contains all queries in qrels, and filter out queries not in qrels
|
||||
results_filtered = {qid: res for qid, res in results.items() if qid in qrels}
|
||||
qrels_filtered = {
|
||||
qid: rel_docs for qid, rel_docs in qrels.items() if qid in results_filtered
|
||||
}
|
||||
|
||||
print(f"After filtering: {len(results_filtered)} results, {len(qrels_filtered)} qrels")
|
||||
|
||||
if len(results_filtered) != len(qrels_filtered):
|
||||
print(
|
||||
f"Warning: Mismatch between results ({len(results_filtered)}) and qrels ({len(qrels_filtered)}) queries"
|
||||
)
|
||||
missing_in_results = set(qrels.keys()) - set(results.keys())
|
||||
if missing_in_results:
|
||||
print(f"Queries in qrels but not in results: {len(missing_in_results)} queries")
|
||||
print(f"First 5 missing queries: {list(missing_in_results)[:5]}")
|
||||
|
||||
# Convert qrels to pytrec_eval format
|
||||
qrels_pytrec = {}
|
||||
for qid, rel_docs in qrels_filtered.items():
|
||||
qrels_pytrec[qid] = dict(rel_docs.items())
|
||||
|
||||
# Evaluate
|
||||
eval_result = calculate_retrieval_scores(
|
||||
results=results_filtered,
|
||||
qrels=qrels_pytrec,
|
||||
k_values=k_values,
|
||||
)
|
||||
|
||||
# Format scores
|
||||
scores = make_score_dict(
|
||||
ndcg=eval_result.ndcg,
|
||||
_map=eval_result.map,
|
||||
recall=eval_result.recall,
|
||||
precision=eval_result.precision,
|
||||
mrr=eval_result.mrr,
|
||||
naucs=eval_result.naucs,
|
||||
naucs_mrr=eval_result.naucs_mrr,
|
||||
cv_recall=eval_result.cv_recall,
|
||||
task_scores={},
|
||||
)
|
||||
|
||||
return scores
|
||||
|
||||
Reference in New Issue
Block a user