Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Kamer/Basic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kamer/Basic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kamer/Basic")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kamer/Basic") model = AutoModelForSequenceClassification.from_pretrained("Kamer/Basic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f99da9dbfc0e738ba751212a3d2864dc60459defc937ad15182cff9af04390e7
- Size of remote file:
- 268 MB
- SHA256:
- d873eef2cda45a3cb577eb15b14803c62ea9c5709ca08341ac4b554d18cd8172
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.