Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use rcade/child_model_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rcade/child_model_learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rcade/child_model_learning")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rcade/child_model_learning") model = AutoModelForSequenceClassification.from_pretrained("rcade/child_model_learning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81968719398322e46bfa51108581922ce018568b0fd36b74ccdc2f44dbadcda6
- Size of remote file:
- 5.18 kB
- SHA256:
- 5b18b5a94cc326eab6c6062805a5ac2df22d7ac1fff0c586b0fe84114ee443e3
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