Remove sidebar

app.py

@@ -54,15 +54,6 @@ st.set_page_config(
 st.title("Multilingual Visual Question Answering")
 st.write("[Gunjan Chhablani](https://huggingface.co/gchhablani), [Bhavitvya Malik](https://huggingface.co/bhavitvyamalik)")
 
-
-st.sidebar.write(read_markdown("about.md"))
-st.sidebar.write(read_markdown("caveats.md"))
-st.sidebar.write(read_markdown("challenges.md"))
-st.sidebar.write(read_markdown("social_impact.md"))
-st.sidebar.write(read_markdown("checkpoints.md"))
-st.sidebar.write(read_markdown("acknowledgements.md"))
-
-
 with st.beta_expander("Usage"):
     st.markdown(read_markdown("usage.md"))
 
@@ -71,8 +62,6 @@ with st.beta_expander("Method"):
     st.markdown(read_markdown("pretraining.md"))
     st.markdown(read_markdown("finetuning.md"))
 
-
-
 first_index = 20
 # Init Session State
 if state.image_file is None:
@@ -130,4 +119,13 @@ logits = softmax(logits)
 labels, values = get_top_5_predictions(logits, answer_reverse_mapping)
 translated_labels = translate_labels(labels, state.answer_lang_id)
 fig = plotly_express_horizontal_bar_plot(values, translated_labels)
-st.plotly_chart(fig, use_container_width = True)
+st.plotly_chart(fig, use_container_width = True)
+
+
+st.write(read_markdown("about.md"))
+st.write(read_markdown("caveats.md"))
+st.write(read_markdown("challenges.md"))
+st.write(read_markdown("social_impact.md"))
+st.write(read_markdown("references.md"))
+st.write(read_markdown("checkpoints.md"))
+st.write(read_markdown("acknowledgements.md"))

sections/caveats.md

@@ -1 +1 @@
-**Caveat**: The best fine-tuned model only achieves 0.49 accuracy on the multilingual validation data that we create. This could be because of not-so-great quality translations, sub-optimal hyperparameters and lack of ample training. In future, we hope to improve this model by addressing such concerns.
+**Caveats**: The best fine-tuned model only achieves 0.49 accuracy on the multilingual validation data that we create. This could be because of not-so-great quality translations, sub-optimal hyperparameters and lack of ample training. In future, we hope to improve this model by addressing such concerns.

sections/references.md

@@ -1 +1,16 @@
 # References
+- [Conceptual 12M Dataset](https://github.com/google-research-datasets/conceptual-12m)
+
+- [VQA v2 Dataset](https://visualqa.org/challenge.html)
+
+- [Hybrid CLIP Example](https://github.com/huggingface/transformers/blob/master/src/transformers/models/clip/modeling_flax_clip.py)
+
+- [VisualBERT Modeling File](https://github.com/huggingface/transformers/blob/master/src/transformers/models/visual_bert/modeling_visual_bert.py)
+
+- [BERT Modeling File](https://github.com/huggingface/transformers/blob/master/src/transformers/models/bert/modeling_flax_bert.py)
+
+- [CLIP Modeling File](https://github.com/huggingface/transformers/blob/master/src/transformers/models/clip/modeling_flax_clip.py)
+
+- [Summarization Training Script](https://github.com/huggingface/transformers/blob/master/examples/flax/summarization/run_summarization_flax.py)
+
+- [MLM Training Script](https://github.com/huggingface/transformers/blob/2df63282e010ac518683252d8ddba21e58d2faf3/examples/flax/language-modeling/run_mlm_flax.py)