Visual Document Retrieval
Safetensors
sentence-transformers
colpali-engine
qwen3_5
vision-language
colbert
late-interaction
multi-vector
vidore
document-retrieval
multimodal
Instructions to use tencent/EVIE-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tencent/EVIE-8B with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("tencent/EVIE-8B") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
File size: 1,340 Bytes
315e4cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | """Refuse to write train/eval/compress products into the Python venv."""
from __future__ import annotations
import os
import sys
from pathlib import Path
def venv_roots() -> list[Path]:
roots: list[Path] = []
venv = os.environ.get("VIRTUAL_ENV")
if venv:
roots.append(Path(venv))
evie = os.environ.get("EVIE_ROOT")
if evie:
for name in ("env", "venv", ".venv", "train-env"):
roots.append(Path(evie) / name)
prefix = Path(sys.prefix)
if (prefix / "pyvenv.cfg").is_file():
roots.append(prefix)
out: list[Path] = []
seen: set[Path] = set()
for raw in roots:
try:
resolved = raw.resolve()
except OSError:
continue
if resolved in seen:
continue
seen.add(resolved)
out.append(resolved)
return out
def forbid_venv_path(path: str | Path, label: str) -> Path:
path = Path(path).expanduser().resolve()
for root in venv_roots():
try:
path.relative_to(root)
except ValueError:
continue
raise ValueError(
f"{label} must not sit inside the Python env ({root}): {path}. "
"Checkpoints -> $RUNS_DIR ($EVIE_ROOT/runs); "
"HF caches -> $EVIE_ROOT/.cache; never $EVIE_ROOT/env."
)
return path
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