Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/DBERT_CleanDesc_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/DBERT_CleanDesc_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/DBERT_CleanDesc_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/DBERT_CleanDesc_v2") model = AutoModelForSequenceClassification.from_pretrained("ratish/DBERT_CleanDesc_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cba87e81d1ff194e3ff817fa37d2af72693050aafe4116ef1f3bbc79b3635d2c
- Size of remote file:
- 268 MB
- SHA256:
- f2d749faa6025521144c701fee24078ca87fa3be4a008a262231261855e0fe0d
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