Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use hr-wesbeaver/test_model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hr-wesbeaver/test_model_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hr-wesbeaver/test_model_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hr-wesbeaver/test_model_1") model = AutoModelForSequenceClassification.from_pretrained("hr-wesbeaver/test_model_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fc337c1eb1edc4f87bd6366d550a61f60c476e966007c921475b599c7c38443
- Size of remote file:
- 4.73 kB
- SHA256:
- e8fcdf7a8691003e64b5bd45a761c5b2369b4f3d0410b7a53b7b00ebafe679e7
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