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:
- 93d58490edc2539c2ae39dda007ab6c95605a0a126dc6f34da38bc13e8c1be56
- Size of remote file:
- 5.11 kB
- SHA256:
- bd8a7d4ab7aa25d0445ffbfbf6631fb08dcc7ab78b34abad3492590731772598
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