Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use k-code/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k-code/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="k-code/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("k-code/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("k-code/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c5282edad9a1521f5727b17dc70a4b8c7ec1adc7c93a5c56138e2ac7ad0fee9
- Size of remote file:
- 5.3 kB
- SHA256:
- 804f42bb9a0eaa1e32b173aeedb55a95e816fdae8aa0a96ed8799374f60600b8
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