reproduce docvqa results and add debug file
This commit is contained in:
yichuan-w
@@ -227,28 +227,26 @@ def _embed_queries(model, processor, queries: list[str]) -> list[Any]:
|
||||
# 2. Manually adds: query_prefix + text + query_augmentation_token * 10
|
||||
# 3. Calls processor.process_queries(batch) where batch is now a list of strings
|
||||
# 4. process_queries adds: query_prefix + text + suffix (suffix = query_augmentation_token * 10)
|
||||
#
|
||||
#
|
||||
# This results in duplicate addition: query_prefix is added twice, query_augmentation_token * 20 total
|
||||
# We need to match this exactly to reproduce MTEB results
|
||||
|
||||
|
||||
all_embeds = []
|
||||
batch_size = 32 # Match MTEB's default batch_size
|
||||
|
||||
|
||||
with torch.no_grad():
|
||||
for i in tqdm(range(0, len(queries), batch_size), desc="Embedding queries"):
|
||||
batch_queries = queries[i:i + batch_size]
|
||||
|
||||
batch_queries = queries[i : i + batch_size]
|
||||
|
||||
# Match MTEB: manually add query_prefix + text + query_augmentation_token * 10
|
||||
# Then process_queries will add them again (resulting in 20 augmentation tokens total)
|
||||
batch = [
|
||||
processor.query_prefix
|
||||
+ t
|
||||
+ processor.query_augmentation_token * 10
|
||||
processor.query_prefix + t + processor.query_augmentation_token * 10
|
||||
for t in batch_queries
|
||||
]
|
||||
inputs = processor.process_queries(batch)
|
||||
inputs = {k: v.to(model.device) for k, v in inputs.items()}
|
||||
|
||||
|
||||
if model.device.type == "cuda":
|
||||
with torch.autocast(
|
||||
device_type="cuda",
|
||||
@@ -257,10 +255,10 @@ def _embed_queries(model, processor, queries: list[str]) -> list[Any]:
|
||||
outs = model(**inputs)
|
||||
else:
|
||||
outs = model(**inputs)
|
||||
|
||||
|
||||
# Match MTEB: convert to float32 on CPU
|
||||
all_embeds.extend(list(torch.unbind(outs.cpu().to(torch.float32))))
|
||||
|
||||
|
||||
return all_embeds
|
||||
|
||||
|
||||
@@ -309,74 +307,82 @@ def _build_fast_plaid_index(
|
||||
) -> tuple[Any, float]:
|
||||
"""
|
||||
Build a Fast-Plaid index from document embeddings.
|
||||
|
||||
|
||||
Args:
|
||||
index_path: Path to save the Fast-Plaid index
|
||||
doc_vecs: List of document embeddings (each is a tensor with shape [num_tokens, embedding_dim])
|
||||
filepaths: List of filepath identifiers for each document
|
||||
images: List of PIL Images corresponding to each document
|
||||
|
||||
|
||||
Returns:
|
||||
Tuple of (FastPlaid index object, build_time_in_seconds)
|
||||
"""
|
||||
import torch
|
||||
from fast_plaid import search as fast_plaid_search
|
||||
|
||||
|
||||
print(f" Preparing {len(doc_vecs)} document embeddings for Fast-Plaid...")
|
||||
_t0 = time.perf_counter()
|
||||
|
||||
|
||||
# Convert doc_vecs to list of tensors
|
||||
documents_embeddings = []
|
||||
for i, vec in enumerate(doc_vecs):
|
||||
if i % 1000 == 0:
|
||||
print(f" Converting embedding {i}/{len(doc_vecs)}...")
|
||||
if not isinstance(vec, torch.Tensor):
|
||||
vec = torch.tensor(vec) if isinstance(vec, np.ndarray) else torch.from_numpy(np.array(vec))
|
||||
vec = (
|
||||
torch.tensor(vec)
|
||||
if isinstance(vec, np.ndarray)
|
||||
else torch.from_numpy(np.array(vec))
|
||||
)
|
||||
# Ensure float32 for Fast-Plaid
|
||||
if vec.dtype != torch.float32:
|
||||
vec = vec.float()
|
||||
documents_embeddings.append(vec)
|
||||
|
||||
|
||||
print(f" Converted {len(documents_embeddings)} embeddings")
|
||||
if len(documents_embeddings) > 0:
|
||||
print(f" First embedding shape: {documents_embeddings[0].shape}")
|
||||
print(f" First embedding dtype: {documents_embeddings[0].dtype}")
|
||||
|
||||
|
||||
# Prepare metadata for Fast-Plaid
|
||||
print(f" Preparing metadata for {len(filepaths)} documents...")
|
||||
metadata_list = []
|
||||
for i, filepath in enumerate(filepaths):
|
||||
metadata_list.append({
|
||||
"filepath": filepath,
|
||||
"index": i,
|
||||
})
|
||||
|
||||
metadata_list.append(
|
||||
{
|
||||
"filepath": filepath,
|
||||
"index": i,
|
||||
}
|
||||
)
|
||||
|
||||
# Create Fast-Plaid index
|
||||
print(f" Creating FastPlaid object with index path: {index_path}")
|
||||
try:
|
||||
fast_plaid_index = fast_plaid_search.FastPlaid(index=index_path)
|
||||
print(f" FastPlaid object created successfully")
|
||||
print(" FastPlaid object created successfully")
|
||||
except Exception as e:
|
||||
print(f" Error creating FastPlaid object: {type(e).__name__}: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
raise
|
||||
|
||||
|
||||
print(f" Calling fast_plaid_index.create() with {len(documents_embeddings)} documents...")
|
||||
try:
|
||||
fast_plaid_index.create(
|
||||
documents_embeddings=documents_embeddings,
|
||||
metadata=metadata_list,
|
||||
)
|
||||
print(f" Fast-Plaid index created successfully")
|
||||
print(" Fast-Plaid index created successfully")
|
||||
except Exception as e:
|
||||
print(f" Error creating Fast-Plaid index: {type(e).__name__}: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
raise
|
||||
|
||||
|
||||
build_secs = time.perf_counter() - _t0
|
||||
|
||||
|
||||
# Save images separately (Fast-Plaid doesn't store images)
|
||||
print(f" Saving {len(images)} images...")
|
||||
images_dir = Path(index_path) / "images"
|
||||
@@ -384,7 +390,7 @@ def _build_fast_plaid_index(
|
||||
for i, img in enumerate(tqdm(images, desc="Saving images")):
|
||||
img_path = images_dir / f"doc_{i}.png"
|
||||
img.save(str(img_path))
|
||||
|
||||
|
||||
return fast_plaid_index, build_secs
|
||||
|
||||
|
||||
@@ -392,30 +398,30 @@ def _fast_plaid_index_exists(index_path: str) -> bool:
|
||||
"""
|
||||
Check if Fast-Plaid index exists by checking for key files.
|
||||
This avoids creating the FastPlaid object which may trigger memory allocation.
|
||||
|
||||
|
||||
Args:
|
||||
index_path: Path to the Fast-Plaid index
|
||||
|
||||
|
||||
Returns:
|
||||
True if index appears to exist, False otherwise
|
||||
"""
|
||||
index_path_obj = Path(index_path)
|
||||
if not index_path_obj.exists() or not index_path_obj.is_dir():
|
||||
return False
|
||||
|
||||
|
||||
# Fast-Plaid creates a SQLite database file for metadata
|
||||
# Check for metadata.db as the most reliable indicator
|
||||
metadata_db = index_path_obj / "metadata.db"
|
||||
if metadata_db.exists() and metadata_db.stat().st_size > 0:
|
||||
return True
|
||||
|
||||
|
||||
# Also check if directory has any files (might be incomplete index)
|
||||
try:
|
||||
if any(index_path_obj.iterdir()):
|
||||
return True
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
return False
|
||||
|
||||
|
||||
@@ -424,20 +430,20 @@ def _load_fast_plaid_index_if_exists(index_path: str) -> Optional[Any]:
|
||||
Load Fast-Plaid index if it exists.
|
||||
First checks if index files exist, then creates the FastPlaid object.
|
||||
The actual index data loading happens lazily when search is called.
|
||||
|
||||
|
||||
Args:
|
||||
index_path: Path to the Fast-Plaid index
|
||||
|
||||
|
||||
Returns:
|
||||
FastPlaid index object if exists, None otherwise
|
||||
"""
|
||||
try:
|
||||
from fast_plaid import search as fast_plaid_search
|
||||
|
||||
|
||||
# First check if index files exist without creating the object
|
||||
if not _fast_plaid_index_exists(index_path):
|
||||
return None
|
||||
|
||||
|
||||
# Now try to create FastPlaid object
|
||||
# This may trigger some memory allocation, but the full index loading is deferred
|
||||
fast_plaid_index = fast_plaid_search.FastPlaid(index=index_path)
|
||||
@@ -459,81 +465,105 @@ def _search_fast_plaid(
|
||||
) -> tuple[list[tuple[float, int]], float]:
|
||||
"""
|
||||
Search Fast-Plaid index with a query embedding.
|
||||
|
||||
|
||||
Args:
|
||||
fast_plaid_index: FastPlaid index object
|
||||
query_vec: Query embedding tensor with shape [num_tokens, embedding_dim]
|
||||
top_k: Number of top results to return
|
||||
|
||||
|
||||
Returns:
|
||||
Tuple of (results_list, search_time_in_seconds)
|
||||
results_list: List of (score, doc_id) tuples
|
||||
"""
|
||||
import torch
|
||||
|
||||
|
||||
_t0 = time.perf_counter()
|
||||
|
||||
|
||||
# Ensure query is a torch tensor
|
||||
if not isinstance(query_vec, torch.Tensor):
|
||||
q_vec_tensor = torch.tensor(query_vec) if isinstance(query_vec, np.ndarray) else torch.from_numpy(np.array(query_vec))
|
||||
q_vec_tensor = (
|
||||
torch.tensor(query_vec)
|
||||
if isinstance(query_vec, np.ndarray)
|
||||
else torch.from_numpy(np.array(query_vec))
|
||||
)
|
||||
else:
|
||||
q_vec_tensor = query_vec
|
||||
|
||||
|
||||
# Fast-Plaid expects shape [num_queries, num_tokens, embedding_dim]
|
||||
if q_vec_tensor.dim() == 2:
|
||||
q_vec_tensor = q_vec_tensor.unsqueeze(0) # [1, num_tokens, embedding_dim]
|
||||
|
||||
|
||||
# Perform search
|
||||
scores = fast_plaid_index.search(
|
||||
queries_embeddings=q_vec_tensor,
|
||||
top_k=top_k,
|
||||
show_progress=True,
|
||||
)
|
||||
|
||||
|
||||
search_secs = time.perf_counter() - _t0
|
||||
|
||||
|
||||
# Convert Fast-Plaid results to same format as LEANN: list of (score, doc_id) tuples
|
||||
results = []
|
||||
if scores and len(scores) > 0:
|
||||
query_results = scores[0]
|
||||
# Fast-Plaid returns (doc_id, score), convert to (score, doc_id) to match LEANN format
|
||||
results = [(float(score), int(doc_id)) for doc_id, score in query_results]
|
||||
|
||||
|
||||
return results, search_secs
|
||||
|
||||
|
||||
def _get_fast_plaid_image(index_path: str, doc_id: int) -> Optional[Image.Image]:
|
||||
"""
|
||||
Retrieve image for a document from Fast-Plaid index.
|
||||
|
||||
|
||||
Args:
|
||||
index_path: Path to the Fast-Plaid index
|
||||
doc_id: Document ID
|
||||
|
||||
doc_id: Document ID returned by Fast-Plaid search
|
||||
|
||||
Returns:
|
||||
PIL Image if found, None otherwise
|
||||
|
||||
Note: Uses metadata['index'] to get the actual file index, as Fast-Plaid
|
||||
doc_id may differ from the file naming index.
|
||||
"""
|
||||
# First get metadata to find the actual index used for file naming
|
||||
metadata = _get_fast_plaid_metadata(index_path, doc_id)
|
||||
if metadata is None:
|
||||
# Fallback: try using doc_id directly
|
||||
file_index = doc_id
|
||||
else:
|
||||
# Use the 'index' field from metadata, which matches the file naming
|
||||
file_index = metadata.get("index", doc_id)
|
||||
|
||||
images_dir = Path(index_path) / "images"
|
||||
image_path = images_dir / f"doc_{doc_id}.png"
|
||||
|
||||
image_path = images_dir / f"doc_{file_index}.png"
|
||||
|
||||
if image_path.exists():
|
||||
return Image.open(image_path)
|
||||
|
||||
# If not found with index, try doc_id as fallback
|
||||
if file_index != doc_id:
|
||||
fallback_path = images_dir / f"doc_{doc_id}.png"
|
||||
if fallback_path.exists():
|
||||
return Image.open(fallback_path)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def _get_fast_plaid_metadata(index_path: str, doc_id: int) -> Optional[dict]:
|
||||
"""
|
||||
Retrieve metadata for a document from Fast-Plaid index.
|
||||
|
||||
|
||||
Args:
|
||||
index_path: Path to the Fast-Plaid index
|
||||
doc_id: Document ID
|
||||
|
||||
|
||||
Returns:
|
||||
Dictionary with metadata if found, None otherwise
|
||||
"""
|
||||
try:
|
||||
from fast_plaid import filtering
|
||||
|
||||
metadata_list = filtering.get(index=index_path, subset=[doc_id])
|
||||
if metadata_list and len(metadata_list) > 0:
|
||||
return metadata_list[0]
|
||||
@@ -1053,18 +1083,18 @@ class ViDoReBenchmarkEvaluator:
|
||||
A reusable class for evaluating ViDoRe benchmarks (v1 and v2).
|
||||
This class encapsulates common functionality for building indexes, searching, and evaluating.
|
||||
"""
|
||||
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str,
|
||||
use_fast_plaid: bool = False,
|
||||
top_k: int = 100,
|
||||
first_stage_k: int = 500,
|
||||
k_values: list[int] = None,
|
||||
k_values: Optional[list[int]] = None,
|
||||
):
|
||||
"""
|
||||
Initialize the evaluator.
|
||||
|
||||
|
||||
Args:
|
||||
model_name: Model name ("colqwen2" or "colpali")
|
||||
use_fast_plaid: Whether to use Fast-Plaid instead of LEANN
|
||||
@@ -1077,19 +1107,21 @@ class ViDoReBenchmarkEvaluator:
|
||||
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)
|
||||
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],
|
||||
@@ -1098,31 +1130,31 @@ class ViDoReBenchmarkEvaluator:
|
||||
) -> 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
|
||||
)
|
||||
@@ -1135,15 +1167,15 @@ class ViDoReBenchmarkEvaluator:
|
||||
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(f"LEANN index built")
|
||||
print("LEANN index built")
|
||||
return retriever, corpus_ids
|
||||
|
||||
|
||||
def search_queries(
|
||||
self,
|
||||
queries: dict[str, str],
|
||||
@@ -1154,34 +1186,34 @@ class ViDoReBenchmarkEvaluator:
|
||||
) -> 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
|
||||
@@ -1194,47 +1226,51 @@ class ViDoReBenchmarkEvaluator:
|
||||
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_all(query_np, topk=self.top_k)
|
||||
|
||||
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: list[int] = None,
|
||||
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:
|
||||
import pytrec_eval
|
||||
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")
|
||||
|
||||
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.")
|
||||
@@ -1246,38 +1282,42 @@ class ViDoReBenchmarkEvaluator:
|
||||
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}
|
||||
|
||||
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")
|
||||
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] = {did: score for did, score in rel_docs.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,
|
||||
@@ -1290,5 +1330,5 @@ class ViDoReBenchmarkEvaluator:
|
||||
cv_recall=eval_result.cv_recall,
|
||||
task_scores={},
|
||||
)
|
||||
|
||||
|
||||
return scores
|
||||
|
||||
Reference in New Issue
Block a user