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:
- 75df60a3cb3892bd866f4ffa39556f424a0e7f8c722b4218ae4d2fc7cc7ea3bc
- Size of remote file:
- 4.03 kB
- SHA256:
- 0cab0aa632b52ae94ea36e63892afcb16eea349fd7791204390e3d97ebda5664
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