Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/DBERT_CleanDesc_Mode_v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/DBERT_CleanDesc_Mode_v10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/DBERT_CleanDesc_Mode_v10")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/DBERT_CleanDesc_Mode_v10") model = AutoModelForSequenceClassification.from_pretrained("ratish/DBERT_CleanDesc_Mode_v10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2e86fcc96e2aaf05c70054b22c916c2faa52eb23370cf380a7112663aead5b5
- Size of remote file:
- 268 MB
- SHA256:
- e9844a28a498d6c7d189951e0f21094cd7b1ac4ddd83a2d04e67515cf181a323
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