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 pytorch_model.bin from Sandrro/text_to_topic: direct link, hf CLI and curl.
- Browser
- Download file 117 MB
-
https://huggingface.co/Sandrro/text_to_topic/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sandrro/text_to_topic/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sandrro/text_to_topic/resolve/main/pytorch_model.bin
117 MB
- Xet hash:
- add7f23a2c793764240ba40c6d3cae3a42e9bc6cc46ed2e1a3d1d5ec799b0220
- Size of remote file:
- 117 MB
- SHA256:
- e65451ce678c5b29820245a9f3b1e2e36d926a0064bdb7d5ead547634690f4fa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.