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") - Notebooks
- Google Colab
- Kaggle
| datasets: | |
| - niltheory/ExistenceTypes | |
| language: | |
| - en | |
| license: cc-by-sa-4.0 | |
| library_name: transformers | |
| tags: | |
| - creative writing | |
| - original ip | |
| # Existence Analysis Model (EAM) | |
| **Created for**: Compendium Terminum, IP | |
| **Base Model**: `bert-large-cased-whole-word-masking` | |
| ## Iterative Development | |
| ### Iteration #1: | |
| - **Initial Model**: Utilized `distilBert` for foundational training. | |
| - **Dataset Size**: 96 entries. | |
| - **Outcome**: Established baseline for accuracy metrics. | |
| ### Iteration #2: | |
| - **Model Upgrade**: Transitioned to `bert-base-uncased` from `distilbert-base-uncased`. | |
| - **Dataset Expansion**: Increased from 96 to 296 entries. | |
| - **Performance**: Improved accuracy scores; identified edge cases for refinement. | |
| ### Iteration #3: | |
| - **Model Upgrade**: Transitioned to `bert-large-cased-whole-word-masking` from `bert-base-uncased`. | |
| - **Advancements**: Enhanced contextual sensitivity and accuracy. | |
| - **Results**: Demonstrated more nuanced understanding and sensitivity in predictions. | |
| ## Observations | |
| - Each iteration has contributed to the model's evolving sophistication, leading to improved interpretive performance and accuracy. | |
| - Continuous evaluation, especially in complex or ambiguous cases, is pivotal for future enhancements. | |
| ## License | |
| This dataset is licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). | |
| Users are free to use, modify, and share it under the same terms, but **commercial use is prohibited**. |