mmBERT-32K Factcheck Classifier (Merged)
This is the merged version of the mmBERT-32K factcheck classifier model, ready for direct inference without PEFT.
Model Details
- Base Model: vllm-sr/mmbert-32k-yarn
- Task: Text Classification
- Number of Labels: 2
- Context Length: 32,768 tokens
- Architecture: ModernBERT with YaRN RoPE scaling
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained(
"vllm-sr/mmbert32k-factcheck-classifier-merged",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("vllm-sr/mmbert32k-factcheck-classifier-merged")
# Inference
inputs = tokenizer("Your text here", return_tensors="pt", truncation=True, max_length=32768)
outputs = model(**inputs)
Related Models
- LoRA Adapter: vllm-sr/mmbert32k-factcheck-classifier-lora
- Base Model: vllm-sr/mmbert-32k-yarn
License
Apache 2.0