Text Classification
Transformers
Safetensors
laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use vdaular/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vdaular/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vdaular/laya")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vdaular/laya", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 308 Bytes
c7b09e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 8192,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "PreTrainedTokenizerFast",
"unk_token": "[UNK]"
} |