broken table fix and missing gif added
@@ -18,6 +18,8 @@ This is the ultimate NLP research tool. You can visualize and compare the result
and can see with your own eyes how context is encoded by transformer models like `BERT` or `XLNET`and many more !
Besides that, you can also compare the results of the 200+ NER models John Snow Labs provides and see how peformances changes with varrying ebeddings, like Contextual, Static and Domain Specific Embeddings.
+<img src="https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/docs/assets/streamlit_docs_assets/gif/start.gif">
+
## Install
[For detailed instructions refer to the NLU install documentation here](https://nlu.johnsnowlabs.com/docs/en/install)
You need `Open JDK 8` installed and the following python packages
@@ -81,6 +83,7 @@ Ontop of that, there are various visualization methods a NLUPipeline provides ea
### Overview of NLU + Streamlit buildingblocks
+
|Method | Description |
|---------------------------------------------------------------|-------------------------------------------|
| [`nlu.load('<Model>').predict(data)`](TODO.com) | Load any of the [1000+ models](https://nlp.johnsnowlabs.com/models) by providing the model name any predict on most Pythontic [data strucutres like Pandas, strings, arrays of strings and more](https://nlu.johnsnowlabs.com/docs/en/predict_api#supported-data-types) |
@@ -92,7 +95,6 @@ Ontop of that, there are various visualization methods a NLUPipeline provides ea
| [`nlu.load('<Model>').viz_streamlit_token(data)`](TODO.com) | Display all detected token features and informations in Streamlit |
| [`nlu.load('<Model>').viz(data, write_to_streamlit=True)`](TODO.com) | Display the raw visualization without any UI elements. See [viz docs for more info](https://nlu.johnsnowlabs.com/docs/en/viz_examples). By default all aplicable nlu model references will be shown. |
| [`nlu.enable_streamlit_caching()`](#test) | Enable caching the `nlu.load()` call. Once enabled, the `nlu.load()` method will automatically cached. **This is recommended** to run first and for large peformance gans |
-<p id="test">TODO ANCHORSZZ</p>
# Detailed visualizer information and API docs
