Text Classification
Transformers
Safetensors
English
distilbert
log-analysis
anomaly-detection
bert
huggingface
Eval Results (legacy)
text-embeddings-inference
Instructions to use vaibhav2507/cloudops-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhav2507/cloudops-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vaibhav2507/cloudops-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vaibhav2507/cloudops-bert") model = AutoModelForSequenceClassification.from_pretrained("vaibhav2507/cloudops-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7a8965e977b2f32e722d9972c4b6113c3e90b8f283da5f883579053dc4e0223
- Size of remote file:
- 536 MB
- SHA256:
- 8edffe43eacf3097b990637fc068288ec48e5f1704b9a53a42f9fa7877f30ccd
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