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 tokenizer_config.json from Berk/assay-compiled-base: direct link, hf CLI and curl.
- Browser
- Download file 412 Bytes
-
https://huggingface.co/Berk/assay-compiled-base/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Berk/assay-compiled-base/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Berk/assay-compiled-base/resolve/main/tokenizer_config.json
412 Bytes
| { | |
| "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]" | |
| } |