Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use miguelpr/distilbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miguelpr/distilbert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="miguelpr/distilbert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("miguelpr/distilbert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("miguelpr/distilbert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c62853389716eb223ad745666f7d5dafc4b65baab48038cd3e12482ad5365faf
- Size of remote file:
- 4.92 kB
- SHA256:
- b9f45cf977ac1d3aaf68b95ec33efe1a7c834606334871d346e9dd633d92ffd0
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