Text Classification
Transformers
Safetensors
English
bert
creative writing
original ip
text-embeddings-inference
Instructions to use niltheory/ExistenceTypesAnalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niltheory/ExistenceTypesAnalysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="niltheory/ExistenceTypesAnalysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("niltheory/ExistenceTypesAnalysis") model = AutoModelForSequenceClassification.from_pretrained("niltheory/ExistenceTypesAnalysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- **Outcome**: Established baseline for accuracy metrics.
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### Iteration #2:
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- **Dataset Expansion**: Increased from 96 to 296 entries.
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- **Performance**: Improved accuracy scores; identified edge cases for refinement.
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- **Outcome**: Established baseline for accuracy metrics.
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### Iteration #2:
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- **Model Upgrade**: Transitioned to `bert-base-uncased` from `distilbert-base-uncased`.
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- **Dataset Expansion**: Increased from 96 to 296 entries.
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- **Performance**: Improved accuracy scores; identified edge cases for refinement.
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