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
Create README.md
Browse filesExistence Analysis Model, Iteration #3
After the initial test, increased the data and made an initial test to see how much score prediction changed for Iteration #2.
Then I switched from bert-case-uncased to bert-large-cased-whole-word-masking as the base model to train it from.
Switching the model out is Iteration #3, which it's more nuanced and sensitive.
Some of the test prediction scores changed little and other changed significantly.