Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AndrewDOrlov/bert-eval-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndrewDOrlov/bert-eval-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndrewDOrlov/bert-eval-256")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndrewDOrlov/bert-eval-256") model = AutoModelForSequenceClassification.from_pretrained("AndrewDOrlov/bert-eval-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11d954ae6d1799c413b00e0aac5205f8dc373da06e6b94ee8e77e10e086fac98
- Size of remote file:
- 3.96 kB
- SHA256:
- 94301aac5381d70901b97571701ae06b65658b69de7e790249e4d54542dd1a51
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