Text Classification
Transformers
Safetensors
English
Chinese
modernbert
reranker
cross-encoder
agent
decision-making
zero-shot-classification
fast-decider
text-embeddings-inference
Instructions to use mkzero/FastDecider-149M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkzero/FastDecider-149M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mkzero/FastDecider-149M")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mkzero/FastDecider-149M") model = AutoModelForSequenceClassification.from_pretrained("mkzero/FastDecider-149M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 570 Bytes
70134a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"backend": "tokenizers",
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"cls_token": "[CLS]",
"is_local": true,
"local_files_only": false,
"mask_token": "[MASK]",
"max_length": 512,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 8192,
"pad_to_multiple_of": null,
"pad_token": "[PAD]",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "[SEP]",
"stride": 0,
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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