Text Classification
Transformers
PyTorch
Enawené-Nawé
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use ziadA123/trainModel_p1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ziadA123/trainModel_p1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ziadA123/trainModel_p1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ziadA123/trainModel_p1") model = AutoModelForSequenceClassification.from_pretrained("ziadA123/trainModel_p1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ad3a3d042b122230927931aa5796e2fc574622ac63b234ca10b4bfba3f57fb8
- Size of remote file:
- 2.69 MB
- SHA256:
- 19f36c8812c10c1961b9de0e8c70839358f8db6ff88154f34a64444cc015ccbd
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