Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Sandrro/text_to_topic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sandrro/text_to_topic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sandrro/text_to_topic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sandrro/text_to_topic") model = AutoModelForSequenceClassification.from_pretrained("Sandrro/text_to_topic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Sandrro/text_to_topic: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/Sandrro/text_to_topic/resolve/main/training_args.bin
- Command line
-
hf download hf://Sandrro/text_to_topic/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sandrro/text_to_topic/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 3e13933f6c0b7bd99e550590a3e47555b8fd676b26ad9d312819c3096c324dcb
- Size of remote file:
- 3.64 kB
- SHA256:
- 7096871b529af8517d524b7c5cbfd0b590f45b69cfc187fbc6157089245baefd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.