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:
- 0aa3ffc004c6c0182346e06ce78f5c470cae6e14e0460115d94a33eef364504d
- Size of remote file:
- 3.44 kB
- SHA256:
- 8e269bb5df75275edb1f5d96bce41c4be221a464a457594c561a93d4eb13ac94
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