Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use universalml/kjl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/kjl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="universalml/kjl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("universalml/kjl") model = AutoModelForSequenceClassification.from_pretrained("universalml/kjl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c3aa9175e0bb1fc4281f4a70aa6a27d4c24d5f4a50aeb07203fee592890d16e
- Size of remote file:
- 268 MB
- SHA256:
- 76c126508af8879322b6752b67526cec3bda4371818885b330051f1c4eacdf5f
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