Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use stevenysy/minilm-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevenysy/minilm-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stevenysy/minilm-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stevenysy/minilm-text-classification") model = AutoModelForSequenceClassification.from_pretrained("stevenysy/minilm-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ce55a3888432d1582da5cd84f9db988fdb6fe8c5ce44d5d6fa49c73506f4b27
- Size of remote file:
- 5.37 kB
- SHA256:
- c56581a712dfd21b66dd1617ddc13bf0df7d0af809ab1d629e92f3dae7003411
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.