Text Classification
Transformers
Safetensors
English
nli
cross-encoder
qwen3.5
reranker
image-text-to-text
Instructions to use AlexWortega/openjev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexWortega/openjev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AlexWortega/openjev")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlexWortega/openjev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """Validated single-image input for Decisions. Images stay out of the text state.""" | |
| import base64 | |
| import binascii | |
| import io | |
| from PIL import Image, UnidentifiedImageError | |
| from decisions_api import ApiError | |
| IMG_MARK = "<<IMG>>" | |
| IMG_BLOCK = "<|vision_start|><|image_pad|><|vision_end|>" | |
| MAX_IMAGE_BYTES = 4 * 1024 * 1024 | |
| MAX_IMAGE_PIXELS = 12_000_000 | |
| def image_input(body): | |
| """Accept base64 or a data URI; never read server paths or fetch remote URLs.""" | |
| value = body.get("image_data") | |
| if value is None: | |
| return None | |
| if not isinstance(value, str) or not value: | |
| raise ApiError(400, "image_data must be one base64 image or image data URI") | |
| if value.startswith("data:"): | |
| header, sep, value = value.partition(",") | |
| if not sep or not header.startswith("data:image/") or not header.endswith(";base64"): | |
| raise ApiError(400, "image_data must use an image base64 data URI") | |
| if len(value) > 4 * ((MAX_IMAGE_BYTES + 2) // 3): | |
| raise ApiError(413, "image_data exceeds 4 MiB decoded") | |
| try: | |
| raw = base64.b64decode(value, validate=True) | |
| if len(raw) > MAX_IMAGE_BYTES: | |
| raise ApiError(413, "image_data exceeds 4 MiB decoded") | |
| with Image.open(io.BytesIO(raw)) as im: | |
| if im.format not in {"JPEG", "PNG", "WEBP"} or getattr(im, "n_frames", 1) != 1: | |
| raise ApiError(400, "image_data must be a single JPEG, PNG or WebP image") | |
| if im.width * im.height > MAX_IMAGE_PIXELS: | |
| raise ApiError(413, "image_data exceeds 12 million pixels") | |
| im.verify() | |
| except (binascii.Error, ValueError, OSError, UnidentifiedImageError, Image.DecompressionBombError) as e: | |
| raise ApiError(400, "image_data is not a valid base64 image") from e | |
| return value | |
| def image_premise(premise): | |
| if premise.count(IMG_MARK) > 1 or "<|image_pad|>" in premise or "<|vision_start|>" in premise: | |
| raise ApiError(400, "Use at most one <<IMG>> marker in the image premise") | |
| return premise.replace(IMG_MARK, IMG_BLOCK) if IMG_MARK in premise else premise.rstrip() + " " + IMG_BLOCK | |