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
Add HPC (BGL) submodel
Browse files- hpc/config.json +0 -0
- hpc/model.safetensors +3 -0
- hpc/special_tokens_map.json +0 -0
- hpc/tokenizer.json +0 -0
- hpc/tokenizer_config.json +0 -0
- hpc/vocab.txt +0 -0
hpc/config.json
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hpc/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:832ad6841f6cc70605db3f899a5c99ccb8e6e97a1fae00cce55ccc1bc7726150
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size 267832560
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hpc/special_tokens_map.json
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hpc/tokenizer.json
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hpc/tokenizer_config.json
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hpc/vocab.txt
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