Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use wnic00/ayame-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wnic00/ayame-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wnic00/ayame-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wnic00/ayame-1") model = AutoModelForSequenceClassification.from_pretrained("wnic00/ayame-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb78b6a9b560edcb7cf32473a3323594f06749ad9a449564827b5985bd170c4b
- Size of remote file:
- 443 MB
- SHA256:
- f7db8d9fb1c24ff16d8bc4be4e167da668085484e7f85fa968005a11b86c4269
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