Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use TuringGame/distilbert-base-uncased-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TuringGame/distilbert-base-uncased-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TuringGame/distilbert-base-uncased-classifier", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TuringGame/distilbert-base-uncased-classifier") model = AutoModelForSequenceClassification.from_pretrained("TuringGame/distilbert-base-uncased-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79de027f8803b16324fc6c45b5920719160a55ba7a263edf7dc7dd47da8e4b89
- Size of remote file:
- 268 MB
- SHA256:
- d7738d2fb90228f72f3f9822f88721786ca4dfba98f20baba76d1cff77d6c0e4
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