metadata
library_name: sklearn
tags:
- sklearn
- skops
- text-classification
Model description
This is a logistic regression model trained with GPT-2 embeddings on imdb dataset. The notebook to generate this model is in this repository and in this kaggle link.
Intended uses & limitations
This model is trained for educational purposes.
Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|---|---|
| memory | |
| steps | [('embedding', HFTransformersLanguage(model_name_or_path='facebook/bart-base')), ('model', LogisticRegression())] |
| verbose | False |
| embedding | HFTransformersLanguage(model_name_or_path='facebook/bart-base') |
| model | LogisticRegression() |
| embedding__model_name_or_path | facebook/bart-base |
| model__C | 1.0 |
| model__class_weight | |
| model__dual | False |
| model__fit_intercept | True |
| model__intercept_scaling | 1 |
| model__l1_ratio | |
| model__max_iter | 100 |
| model__multi_class | auto |
| model__n_jobs | |
| model__penalty | l2 |
| model__random_state | |
| model__solver | lbfgs |
| model__tol | 0.0001 |
| model__verbose | 0 |
| model__warm_start | False |
Model Plot
The model plot is below.
Pipeline(steps=[('embedding',HFTransformersLanguage(model_name_or_path='facebook/bart-base')),('model', LogisticRegression())])Please rerun this cell to show the HTML repr or trust the notebook.Pipeline(steps=[('embedding',HFTransformersLanguage(model_name_or_path='facebook/bart-base')),('model', LogisticRegression())])HFTransformersLanguage(model_name_or_path='facebook/bart-base')
LogisticRegression()
Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|---|---|
| f1_score | 0.867535 |
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
[More Information Needed]
# Additional Content
## Confusion matrix
