Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hansaht/Text_classification_model_1_pytorch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hansaht/Text_classification_model_1_pytorch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hansaht/Text_classification_model_1_pytorch")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hansaht/Text_classification_model_1_pytorch") model = AutoModelForSequenceClassification.from_pretrained("Hansaht/Text_classification_model_1_pytorch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e7abe087e3273c366f28b5d988996ce5e269b31f4a3d74e12ffd307d34ef047
- Size of remote file:
- 268 MB
- SHA256:
- 5c6bfade12a2a71138b60d8e092804589cb05e5b4281d958a559709da27dcd11
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