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: 412 Bytes
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"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 1000000000000000019884624838656,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "PreTrainedTokenizerFast",
"unk_token": "[UNK]"
} |