Zero-Shot Classification
Transformers
Safetensors
English
matilda_jev
feature-extraction
decision-model
typed-decisions
jev
maincode
custom_code
Instructions to use Maincode/Matilda-Jev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/Matilda-Jev-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Maincode/Matilda-Jev-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/Matilda-Jev-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 603 Bytes
d10ad42 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | """Maincode MATILDA configuration aliases for the Transformers backbone."""
from transformers.models.qwen3_5.configuration_qwen3_5 import (
Qwen3_5Config, Qwen3_5TextConfig, Qwen3_5VisionConfig,
)
class MatildaJevTextConfig(Qwen3_5TextConfig):
model_type = "matilda_jev_text"
class MatildaJevVisionConfig(Qwen3_5VisionConfig):
model_type = "matilda_jev_vision"
class MatildaJevConfig(Qwen3_5Config):
model_type = "matilda_jev"
sub_configs = {"text_config": MatildaJevTextConfig, "vision_config": MatildaJevVisionConfig}
MatildaJevConfig.register_for_auto_class("AutoConfig")
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