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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 578fbaf651f109e8ce785da6bfccde1af79f92730b67baa8ff0fb8b8d51b5c9e
- Size of remote file:
- 433 MB
- SHA256:
- 953abc8fac181395fb624cb99043d235afa7519cb1adad0f55bd4763cb457625
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