Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use rcade/test_falcon_model_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rcade/test_falcon_model_learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rcade/test_falcon_model_learning")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rcade/test_falcon_model_learning") model = AutoModelForSequenceClassification.from_pretrained("rcade/test_falcon_model_learning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5451962432c9bc85f97995e9805bf1e131a725525a322c6d36a53ce9f52d4481
- Size of remote file:
- 4.79 kB
- SHA256:
- aef3260a7cfe49a68066a0046081aa986cefffe2358a972127efb63f2a367a6c
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