Instructions to use Kushrjain/custom_with_100_dim_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kushrjain/custom_with_100_dim_embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kushrjain/custom_with_100_dim_embedding")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kushrjain/custom_with_100_dim_embedding") model = AutoModelForSequenceClassification.from_pretrained("Kushrjain/custom_with_100_dim_embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ae0a1c8c94607cdf2867223fcd00ad9f4bef27b8647fe5cd8faa10bc9287dba
- Size of remote file:
- 712 MB
- SHA256:
- cd01d850ec39af5d311433a8ae7ead510731b36c316ca7aaf780afb3d3555fb0
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