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 calibration.json from Berk/assay-compiled-base: direct link, hf CLI and curl.
- Browser
- Download file 866 Bytes
-
https://huggingface.co/Berk/assay-compiled-base/resolve/main/calibration.json
- Command line
-
hf download hf://Berk/assay-compiled-base/calibration.json
-
curl -L -o calibration.json https://huggingface.co/Berk/assay-compiled-base/resolve/main/calibration.json
866 Bytes
| { | |
| "temperature": 1.4849057900627562, | |
| "before": { | |
| "count": 5513, | |
| "accuracy": 0.6716851079267186, | |
| "brier": 0.45613250785984416, | |
| "brier_soft": 0.43051378112146826, | |
| "nll": 0.8780634493065734, | |
| "nll_soft": 0.8966669647794041, | |
| "ece": 0.09598293395855888, | |
| "mean_confidence": 0.7660815099876934, | |
| "confident_error_rate": 0.0511518229639035, | |
| "score_mae": 0.597205044838637, | |
| "evidence_auc": 0.46369209809264306 | |
| }, | |
| "after": { | |
| "count": 5513, | |
| "accuracy": 0.6716851079267186, | |
| "brier": 0.4471471464049214, | |
| "brier_soft": 0.41946137922607474, | |
| "nll": 0.8275972595200731, | |
| "nll_soft": 0.8401256741826928, | |
| "ece": 0.04235756362376845, | |
| "mean_confidence": 0.6827506249377135, | |
| "confident_error_rate": 0.023036459278070017, | |
| "score_mae": 0.6329812375448026, | |
| "evidence_auc": 0.46369209809264306 | |
| } | |
| } |