Text Classification
Transformers
ONNX
Safetensors
multilingual
modernbert
mmbert-32k
yarn-rope
merged-model
text-embeddings-inference
Instructions to use vllm-sr/mmbert32k-factcheck-classifier-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/mmbert32k-factcheck-classifier-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vllm-sr/mmbert32k-factcheck-classifier-merged")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/mmbert32k-factcheck-classifier-merged") model = AutoModelForSequenceClassification.from_pretrained("vllm-sr/mmbert32k-factcheck-classifier-merged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K