Text Classification
Transformers
Safetensors
English
bert
bert-base-uncased
text-embeddings-inference
Instructions to use disham993/electrical-classification-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use disham993/electrical-classification-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="disham993/electrical-classification-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-classification-bert-base") model = AutoModelForSequenceClassification.from_pretrained("disham993/electrical-classification-bert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f35e6852c900331e05d8b1cc8a63b0f5f0c3f0828dc92ba473a7e85e99615a4
- Size of remote file:
- 438 MB
- SHA256:
- d066218be75e309252cd94780220b18b07dbbb829e242578a3aabd664eb99d1f
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