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
BertAbstractIntroduction / runs /Jun09_18-09-25_51a1b99e6f93 /events.out.tfevents.1717956566.51a1b99e6f93.34.0
- Xet hash:
- d550c2149b8ad360f605e89b7a521121797aa3ba41b4f098c7dedc28131000bf
- Size of remote file:
- 16.1 kB
- SHA256:
- 373cef5e8ac02901f97ef344831d6bf5787b3d7a40aabdacf62b061f77a649ae
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