Text Classification
Transformers
PyTorch
Safetensors
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use NicholasSynovic/AutoTrain-LUC-COMP429-VEAA-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NicholasSynovic/AutoTrain-LUC-COMP429-VEAA-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NicholasSynovic/AutoTrain-LUC-COMP429-VEAA-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NicholasSynovic/AutoTrain-LUC-COMP429-VEAA-Classification") model = AutoModelForSequenceClassification.from_pretrained("NicholasSynovic/AutoTrain-LUC-COMP429-VEAA-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8eaac7a0ecbef878357c9c4433a3cb9b8246a53d2b0d69b9c82b397f8f60ee06
- Size of remote file:
- 712 kB
- SHA256:
- cb374d6bc042c22455946f4e09a89d29882a199fdaf8fb25be00dc8b8857a448
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