Zero-Shot Classification
Transformers
Safetensors
English
qwen3
text-generation
assay
decision-model
calibrated
conformal-prediction
text-classification
structured-output
text-generation-inference
Instructions to use Berk/assay-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Berk/assay-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Berk/assay-0.6b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Berk/assay-0.6b") model = AutoModelForCausalLM.from_pretrained("Berk/assay-0.6b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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