Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use MarPla/BertAbstractIntroduction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarPla/BertAbstractIntroduction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MarPla/BertAbstractIntroduction")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MarPla/BertAbstractIntroduction") model = AutoModelForSequenceClassification.from_pretrained("MarPla/BertAbstractIntroduction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20dd3d910963fc665b284555558771a8c9441c3fa6a068b330729ea76b3923ba
- Size of remote file:
- 438 MB
- SHA256:
- 8e7efa8ea474bd76a063a95ad1ea2ff5bdf63235bf5c1d509aefd6d0d035d0b4
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