Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use kalisia/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kalisia/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kalisia/content")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kalisia/content") model = AutoModelForSequenceClassification.from_pretrained("kalisia/content", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d83d6b8994528a140d72d1e5b3b2d3e014976b70b1b05503969a294b04ed0e2a
- Size of remote file:
- 5.11 kB
- SHA256:
- 730fa7c53cdb253eb5fa7a611c7a9cd79cb2e9740a87f8d79d0ab411dbdcdcfd
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