Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
nlu
Eval Results (legacy)
text-embeddings-inference
Instructions to use cartesinus/bert-base-uncased-amazon-massive-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cartesinus/bert-base-uncased-amazon-massive-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cartesinus/bert-base-uncased-amazon-massive-intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cartesinus/bert-base-uncased-amazon-massive-intent") model = AutoModelForSequenceClassification.from_pretrained("cartesinus/bert-base-uncased-amazon-massive-intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 547b8962d8cd5db601922ca5992baf8bb9f72337dee2798bfe9af062f58e084d
- Size of remote file:
- 438 MB
- SHA256:
- 5724d050ed0ed34e80991d490fbea80c6427a96075936c40815a53c7268e818b
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