Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use cruiser/final_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cruiser/final_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cruiser/final_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cruiser/final_model") model = AutoModelForSequenceClassification.from_pretrained("cruiser/final_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cd9a612edc6173df1eab609751bb38133439e832f978ecae6aafb97fdb455c4
- Size of remote file:
- 438 MB
- SHA256:
- 8cf7f26243f509eaac479c0925364f8947593f2cfc7e6d8ee4178e742be15c8d
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