Text Classification
Transformers
Safetensors
laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use GeekyAbs/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GeekyAbs/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GeekyAbs/laya")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GeekyAbs/laya", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer/tokenizer_config.json from GeekyAbs/laya: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
-
https://huggingface.co/GeekyAbs/laya/resolve/main/tokenizer/tokenizer_config.json
- Command line
-
hf download hf://GeekyAbs/laya/tokenizer/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/GeekyAbs/laya/resolve/main/tokenizer/tokenizer_config.json
308 Bytes
| { | |
| "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]" | |
| } |