Text Classification
Transformers
PyTorch
bert
Generated from Trainer
sibyl
Eval Results (legacy)
text-embeddings-inference
Instructions to use fabriceyhc/bert-base-uncased-ag_news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fabriceyhc/bert-base-uncased-ag_news with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fabriceyhc/bert-base-uncased-ag_news")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fabriceyhc/bert-base-uncased-ag_news") model = AutoModelForSequenceClassification.from_pretrained("fabriceyhc/bert-base-uncased-ag_news", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c0b11985594a49b567753483bb100cf19a67c0f1550682dd674c7740f3b0afc
- Size of remote file:
- 2.61 kB
- SHA256:
- 7d6221d25e46c88c787b921c8f49f8cd6f2d7b89294c307b6d4c95548bd83311
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