Text Classification
Transformers
PyTorch
Safetensors
roberta
fill-mask
multi-label-classification
builders-risk
text-embeddings-inference
Instructions to use leblanciii/trident-peril-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leblanciii/trident-peril-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leblanciii/trident-peril-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("leblanciii/trident-peril-classifier") model = AutoModelForMaskedLM.from_pretrained("leblanciii/trident-peril-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86ea740d08356e20a972623dc42c97b7d07c070436c5fdd3c66ad369570ae376
- Size of remote file:
- 499 MB
- SHA256:
- a1e6dab33799bd40532c5ba7547e1c9281db0f9e34ae612266e1819690113b4c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.