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:
- 7a9450a1eff1012720b24ad83adb0d590dff01fa975dec0bb4c481216e5526b6
- Size of remote file:
- 712 MB
- SHA256:
- 801e65a23adc6335538d0966845614dea1e31f0fff10b2b9102ffd2e2cbbe623
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