Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use angusan/text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use angusan/text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="angusan/text_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("angusan/text_classification") model = AutoModelForSequenceClassification.from_pretrained("angusan/text_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f59462e4a6db19095403c3fbdc0981b264e747c6bc2a3b60a8e795dcc8ad2f7e
- Size of remote file:
- 4.98 kB
- SHA256:
- 1c76e658633d0ea62fdb6edd48091ee1c4f32750fb80c3551ea7a54f1fdd1d52
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.