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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51f5023a87b9b5cb581f53c83c616e463a3b6a1e707cb300ad9fea6654ad0d5a
- Size of remote file:
- 268 MB
- SHA256:
- 908d8ef3c27aa917d17d6e03aa40fc910ceec74b6f8035b14d95d0e3a5b2a70c
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