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:
- 7f4c4635ab056526c106a941ca8155b596a46029fd845fbb6fda67c62adf6c99
- Size of remote file:
- 3.96 kB
- SHA256:
- 48582f892303b703a44b8f94f9bce19b2895393070e258806472246aa9479fc0
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