Instructions to use ArunAIML/bert-model-intent-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArunAIML/bert-model-intent-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ArunAIML/bert-model-intent-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ArunAIML/bert-model-intent-classification") model = AutoModelForSequenceClassification.from_pretrained("ArunAIML/bert-model-intent-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f8886c7f4b0654048c2f9b69ce2ec2a034fcce993eff4995d4b03adc71760dc
- Size of remote file:
- 5.3 kB
- SHA256:
- 40f48b71da43a9ad0d0f2a5e2e1babede0648d53e3e5c3deb2d72c0fdfb0aa7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.