Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/DBERT_ZS_CleanCollision_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/DBERT_ZS_CleanCollision_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/DBERT_ZS_CleanCollision_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/DBERT_ZS_CleanCollision_v1") model = AutoModelForSequenceClassification.from_pretrained("ratish/DBERT_ZS_CleanCollision_v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81703c0273d763d6710cabd09991b9cf3bd5cbbaf14e71101f3ac6d9ebdb9224
- Size of remote file:
- 268 MB
- SHA256:
- 829b60c50bca258c1b5f2151f4d9b4a1e760569f54f7e72847c73433b7a648a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.