Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Izarel/distilbert-base-uncased_fine_tuned_title with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Izarel/distilbert-base-uncased_fine_tuned_title with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Izarel/distilbert-base-uncased_fine_tuned_title")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Izarel/distilbert-base-uncased_fine_tuned_title") model = AutoModelForSequenceClassification.from_pretrained("Izarel/distilbert-base-uncased_fine_tuned_title") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2645d957f4cc932356d7666df88f2a0f69741a093d025c8a69b9c0238348c982
- Size of remote file:
- 3.31 kB
- SHA256:
- 9d5c4b985c16332fb3e2be8859d071eb79e72bd74b250d85a8dcdac128d7c51c
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