Added model hub links

apps/classifier.py

@@ -20,17 +20,30 @@ def load_model(input_text, model_name_or_path):
 def app():
     st.title("RoBERTa Marathi")
 
+    st.markdown(
+        "This demo uses [RoBERTa for Marathi](https://huggingface.co/flax-community/roberta-base-mr) model "
+        "trained on [mC4](https://huggingface.co/datasets/mc4)."
+    )
+
+    st.markdown(
+        "Can't figure out where to get a sample text? Visit this "
+        "[link](https://maharashtratimes.com/entertainment/articlelist/19359255.cms), copy any headline and see if "
+        "the model is predicting it as `entertainment` or not."
+    )
+
     classifier = st.sidebar.selectbox("Select a Model", index=0, options=["Indic NLP", "iNLTK"])
 
     sample_texts = [
-        "दानिश सिद्दीकीच्या मृत्यूला आम्ही जबाबदार नाही",
         "अध्यक्ष शरद पवार आणि उपमुख्यमंत्री अजित पवार यांची भेट घेतली.",
         "मोठी बातमी! उद्या दुपारी १ वाजता जाहीर होणार दहावीचा निकाल",
+        "Custom",
     ]
     model_name_or_path = cfg["models"][classifier]
 
-    input_text = st.sidebar.selectbox("Select a Text", options=sample_texts)
-    text_to_classify = st.text_input("Text:", input_text)
+    text_to_classify = st.selectbox("Select a Text", options=sample_texts, index=len(sample_texts) - 1)
+
+    if text_to_classify == "Custom":
+        text_to_classify = st.text_input("Enter custom text:")
 
     predict_button = st.button("Predict")
 

apps/mlm.py

@@ -15,12 +15,22 @@ def load_model(input_text, model_name_or_path):
     nlp = pipeline("fill-mask", model=model, tokenizer=tokenizer)
     result = nlp(input_text)
     sentence, mask = result[0]["sequence"], result[0]["token_str"]
-    return sentence, mask
+    return sentence, mask, result
 
 
 def app():
     st.title("RoBERTa Marathi")
 
+    st.markdown(
+        "This demo uses [RoBERTa for Marathi](https://huggingface.co/flax-community/roberta-base-mr) model "
+        "trained on [mC4](https://huggingface.co/datasets/mc4)."
+    )
+
+    st.markdown(
+        "Can't figure out where to get a sample text? Visit this "
+        "[link](https://maharashtratimes.com/entertainment/articlelist/19359255.cms), copy any headline and mask a word."
+    )
+
     masked_texts = [
         "मोठी बातमी! उद्या दुपारी <mask> वाजता जाहीर होणार दहावीचा निकाल",
         "अध्यक्ष <mask> पवार आणि उपमुख्यमंत्री अजित पवार यांची भेट घेतली.",
@@ -33,7 +43,9 @@ def app():
 
     if fill_button:
         with st.spinner("Filling the Mask..."):
-            filled_sentence, mask = load_model(masked_text, cfg["models"]["RoBERTa"])
+            filled_sentence, mask, raw_json = load_model(masked_text, cfg["models"]["RoBERTa"])
 
             st.markdown(f"**Filled sentence: **{filled_sentence}")
             st.markdown(f"**Predicted masked token: **{mask}")
+
+            st.write(raw_json)