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:
- ebea02968687d63c41e73e58f030c610e1ce5bccf8c02523bcd44b17dc697db4
- Size of remote file:
- 438 MB
- SHA256:
- ab40d1891909c8ae116a6e19eb3672cb464a3ab899c5d1d8a49146dcd58d6851
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