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:
- 3a91562637b20cfd741252740c52d6a8c879cc2827752ff0ec6bc863852562b0
- Size of remote file:
- 3.38 kB
- SHA256:
- 1b181388c6853e6d8873c1706b3dbeb34fe431d9e285eacbc255a0ee3697e8b1
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