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
| { | |
| "bos_token": "<bos>", | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "<bos>", | |
| "eos_token": "<eos>", | |
| "extra_special_tokens": { | |
| "extra_0": "<start_of_turn>", | |
| "extra_1": "<end_of_turn>" | |
| }, | |
| "mask_token": "<mask>", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 8192, | |
| "pad_token": "<pad>", | |
| "padding_side": "right", | |
| "sep_token": "<eos>", | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "<unk>" | |
| } |