Feature Extraction
Transformers
Safetensors
modernbert
typed-decisions
decision-index
cross-encoder
text-embeddings-inference
Instructions to use tasksource/tasksource-decider-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tasksource/tasksource-decider-nano with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tasksource/tasksource-decider-nano")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tasksource/tasksource-decider-nano") model = AutoModel.from_pretrained("tasksource/tasksource-decider-nano", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 489 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]",
"max_length": 7999,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 7999,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"stride": 0,
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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