Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Kamer/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kamer/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kamer/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kamer/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("Kamer/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ef33796bf66a36ce8d499376e54e179d258dd763ca9c4f2eebc1166967d8d99
- Size of remote file:
- 268 MB
- SHA256:
- 29b866f8343cde3fd68f53cda003661f7e20a9849941cb815bd7ec140fbaf0dd
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