Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use letingliu/holder_type with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use letingliu/holder_type with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="letingliu/holder_type")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("letingliu/holder_type") model = AutoModelForSequenceClassification.from_pretrained("letingliu/holder_type") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 092024b7026a6e9a39e15f9476909476293d1f5b3daed106b205100f182b8c3c
- Size of remote file:
- 268 MB
- SHA256:
- 6970059779f979253f95df7415b4c4de34e0171f1941e7e399e15158dc0fd8ab
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