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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a742542f27d075e1aabe1426e8bd9a024e152a799f35e6bdde041b1560c8e18
- Size of remote file:
- 433 MB
- SHA256:
- 78792fba242af7b5486840d034780d4c717c9192b5c216c573b6f971f6b43306
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