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  ---
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  datasets:
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- - niltheory/ExistenceTypes
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  language:
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- - en
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  ---
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- # Existence Analysis Model
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- ## Created for: Compendium Terminum, IP.
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- Iteration #1:
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- base distilBert used to train the model with a data training of 96.
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- Iteration #2:
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- Increased the data training from 96 to 296.
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- Which greatly increased the accuracy scores, albeit there is some edge cases.
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- Iteration #3:
 
 
 
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- Switched from bert-base-uncased to bert-large-cased-whole-word-masking.
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- Which it has more contextual sensitivity and is slightly more accurate than Iteration #2.
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- It's overall more nuanced and sensitive.
 
 
 
 
 
 
 
 
 
 
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  ---
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  datasets:
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+ - niltheory/ExistenceTypes
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  language:
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+ - en
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  ---
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+ # Existence Analysis Model (EAM)
 
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+ **Created for**: Compendium Terminum, IP
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+ **Base Model**: `bert-large-cased-whole-word-masking`
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+ ## Iterative Development
 
 
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+ ### Iteration #1:
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+ - **Initial Model**: Utilized `distilBert` for foundational training.
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+ - **Dataset Size**: 96 entries.
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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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+
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+ ### Iteration #3:
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+ - **Model Upgrade**: Transitioned to `bert-large-cased-whole-word-masking` from `bert-base-uncased`.
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+ - **Advancements**: Enhanced contextual sensitivity and accuracy.
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+ - **Results**: Demonstrated more nuanced understanding and sensitivity in predictions.
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+
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+ ## Observations
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+ - Each iteration has contributed to the model's evolving sophistication, leading to improved interpretive performance and accuracy.
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+ - Continuous evaluation, especially in complex or ambiguous cases, is pivotal for future enhancements.