Token Classification
Transformers
ONNX
Safetensors
English
kompress_v2
text-compression
modernbert
lora
kompress
Instructions to use ooognicki/weeizer-v2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ooognicki/weeizer-v2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ooognicki/weeizer-v2-base")# Load model directly from transformers import HeadroomCompressorV2 model = HeadroomCompressorV2.from_pretrained("ooognicki/weeizer-v2-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 432 Bytes
a24924f | 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 | {
"model_type": "kompress_v2",
"base_model_name": "answerdotai/ModernBERT-base",
"num_labels": 2,
"use_lora": true,
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"lora_target_modules": [
"Wqkv",
"Wi",
"Wo"
],
"span_hidden": 256,
"span_kernels": [
5,
3
],
"span_loss_weight": 0.3,
"head_dropout": 0.1,
"max_length": 8192,
"architectures": [
"HeadroomCompressorV2"
]
} |