fix(api): levelup save and display to top-level

README.md

@@ -82,7 +82,7 @@ Perfect! Now `embed_model` and `labeled_data` are given by you already, simply d
 ```python
 import finetuner
 
-finetuner.fit(
+tuned_model, _ = finetuner.fit(
     embed_model,
     train_data=labeled_data
 )
@@ -96,7 +96,7 @@ You can use Finetuner to interactive label data and train `embed_model` as below
 ```python
 import finetuner
 
-finetuner.fit(
+tuned_model, _ = finetuner.fit(
     embed_model,
     train_data=unlabeled_data,
     interactive=True
@@ -111,7 +111,7 @@ worries, Finetuner can convert your model into an embedding model and train it v
 ```python
 import finetuner
 
-finetuner.fit(
+tuned_model, _ = finetuner.fit(
     general_model,
     train_data=labeled_data,
     to_embedding_model=True,
@@ -127,7 +127,7 @@ worries, Finetuner can help you train an embedding model with interactive labeli
 ```python
 import finetuner
 
-finetuner.fit(
+tuned_model, _ = finetuner.fit(
     general_model,
     train_data=unlabeled_data,
     interactive=True,

docs/basics/fit.md

@@ -3,4 +3,28 @@
 ```{include} ../index.md
 :start-after: <!-- start fit-method -->
 :end-before: <!-- end fit-method -->
+```
+
+## Save model
+
+```python
+import finetuner
+
+finetuner.save(model, './saved-model')
+```
+
+```{caution}
+Depending on your framework, `save` can behave differently. Tensorflow/Keras saves model architecture with the parameters, whereas PyTorch & Paddle only saves the trained parameters.
+```
+
+## Display a model
+
+```python
+import finetuner
+
+finetuner.display(model)
+```
+
+```{caution}
+Depending on your framework, `display` may require different argument for rendering the model correctly. In PyTorch & Paddle, you will also need to give the `input_size` and sometimes `input_dtype` to correctly render the model.
 ```

docs/components/tailor.md

@@ -39,7 +39,7 @@ In general, you do not need to call `to_embedding_model` manually. You can use i
 
 ## `display` method
 
-Tailor also provides a helper function `finetuner.tailor.display()` that gives a table summary of a Keras/PyTorch/Paddle model.
+Tailor also provides a helper function `finetuner.display()` that gives a table summary of a Keras/PyTorch/Paddle model.
 
 Let's see how to use them in action.
 

docs/components/tuner.md

@@ -65,7 +65,7 @@ Although siamese and triplet loss works on pair and triplet inputs respectively,
 
 ## `save` method
 
-After a model is tuned, you can save it by calling `finetuner.tuner.save(model, save_path)`.
+After a model is tuned, you can save it by calling `finetuner.save(model, save_path)`.
 
 
 ## Examples

docs/index.md

@@ -136,7 +136,7 @@ Perfect! Now `embed_model` and `train_data` are already provided by you, simply
 ```python
 import finetuner
 
-finetuner.fit(
+tuned_model, _ = finetuner.fit(
     embed_model,
     train_data=train_data
 )
@@ -159,7 +159,7 @@ emphasize-lines: 6
 ---
 import finetuner
 
-finetuner.fit(
+tuned_model, _ = finetuner.fit(
     embed_model,
     train_data=unlabeled_data,
     interactive=True
@@ -183,7 +183,7 @@ emphasize-lines: 6, 7
 ---
 import finetuner
 
-finetuner.fit(
+tuned_model, _ = finetuner.fit(
     general_model,
     train_data=labeled_data,
     to_embedding_model=True,
@@ -208,7 +208,7 @@ emphasize-lines: 6, 7
 ---
 import finetuner
 
-finetuner.fit(
+tuned_model, _ = finetuner.fit(
     general_model,
     train_data=labeled_data,
     interactive=True,

finetuner/__init__.py

@@ -113,3 +113,8 @@ def fit(
         from .tuner import fit
 
         return model, fit(model, train_data, *args, **kwargs)
+
+
+# level them up to the top-level
+from .tuner import save
+from .tailor import display