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
Download train_args.json from Berk/assay-compiled-base: direct link, hf CLI and curl.
- Browser
- Download file 542 Bytes
-
https://huggingface.co/Berk/assay-compiled-base/resolve/main/train_args.json
- Command line
-
hf download hf://Berk/assay-compiled-base/train_args.json
-
curl -L -o train_args.json https://huggingface.co/Berk/assay-compiled-base/resolve/main/train_args.json
542 Bytes
| { | |
| "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 | |
| } |