Zero-Shot Classification
Transformers
Safetensors
English
modernbert
feature-extraction
assay
decision-model
calibrated
conformal-prediction
text-classification
cpu
Instructions to use Berk/assay-compiled-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Berk/assay-compiled-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Berk/assay-compiled-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Berk/assay-compiled-base") model = AutoModel.from_pretrained("Berk/assay-compiled-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 542 Bytes
003acf8 | 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 26 | {
"encoder": "Alibaba-NLP/gte-modernbert-base",
"data": "data/v4",
"extra": [
"data/distill/generic.jsonl"
],
"out": "runs/compiled-late-gte-base",
"lr": 5e-05,
"head_lr": 0.0005,
"epochs": 3.0,
"batch_size": 32,
"warmup": 0.05,
"evidence_weight": 0.5,
"score_sigma": 0.5,
"hard_targets": false,
"max_state_tokens": 512,
"slots": 8,
"limit": null,
"log_every": 50,
"seed": 0,
"zero_shot_only": false,
"arch": "compiled",
"pair_budget": 256,
"checkpoint_every": 500,
"late_interaction": true
} |