Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/DBERT_CleanDesc_Mode_v3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/DBERT_CleanDesc_Mode_v3.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/DBERT_CleanDesc_Mode_v3.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/DBERT_CleanDesc_Mode_v3.0") model = AutoModelForSequenceClassification.from_pretrained("ratish/DBERT_CleanDesc_Mode_v3.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 00f1bc231d4fe89d9862fac9c476321ac070e355dff3684c87e0d1bf8b4311fa
- Size of remote file:
- 268 MB
- SHA256:
- 42a64c8e5ecade1fc552411a786ff37347565eb6c7140103e2f1db636b0b81b3
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