Update app.py

app.py

@@ -32,10 +32,10 @@ st.title("📘Named Entity Recognition Tagger")
 @st.cache(allow_output_mutation=True)
 def load_model():
 
-    model = AutoModelForTokenClassification.from_pretrained("vonewman/xlm-roberta-base-finetuned-wolof")
+    model = AutoModelForTokenClassification.from_pretrained("vonewman/wolof-finetuned-ner")
     trainer = Trainer(model=model)
 
-    tokenizer = AutoTokenizer.from_pretrained("vonewman/xlm-roberta-base-finetuned-wolof")
+    tokenizer = AutoTokenizer.from_pretrained("vonewman/wolof-finetuned-ner")
 
     return trainer, model, tokenizer
 
@@ -90,13 +90,16 @@ if submit_button:
 
         with c1:
             #csvbutton = download_button(results, "results.csv", "📥 Download .csv")
-            csvbutton = st.download_button(label="📥 Download .csv", data=convert_df(results), file_name= "results.csv", mime='text/csv', key='csv')
+            csvbutton = st.download_button(label="📥 Download .csv", data=convert_df(results), 
+                                           file_name= "results.csv", mime='text/csv', key='csv')
         with c2:
             #textbutton = download_button(results, "results.txt", "📥 Download .txt")
-            textbutton = st.download_button(label="📥 Download .txt", data=convert_df(results), file_name= "results.text", mime='text/plain',  key='text')
+            textbutton = st.download_button(label="📥 Download .txt", data=convert_df(results), 
+                                            file_name= "results.text", mime='text/plain',  key='text')
         with c3:
             #jsonbutton = download_button(results, "results.json", "📥 Download .json")
-            jsonbutton = st.download_button(label="📥 Download .json", data=convert_json(results), file_name= "results.json", mime='application/json',  key='json')
+            jsonbutton = st.download_button(label="📥 Download .json", data=convert_json(results), 
+                                            file_name= "results.json", mime='application/json',  key='json')
 
         st.header("")
         
@@ -114,10 +117,9 @@ with st.expander("ℹ️ - About this app", expanded=True):
 
     st.write(
         """     
--   The **Named Entity Recognition Tagger** app is a tool that performs named entity recognition.
--   The available entitites are: *corporation*, *creative-work*, *group*, *location*, *person* and *product*.
--   The app uses the [RoBERTa model](https://huggingface.co/roberta-large), fine-tuned on the [wnut](https://huggingface.co/datasets/wnut_17) dataset.      
--   The model uses the **byte-level BPE tokenizer**. Each sentece is first tokenized.
--   For more info regarding the data science part, check this [post](https://towardsdatascience.com/named-entity-recognition-with-deep-learning-bert-the-essential-guide-274c6965e2d?sk=c3c3699e329e45a8ed93d286ae04ef10).      
+-   The **Named Entity Recognition Wolof** app is a tool that performs named entity recognition in Wolof.
+-   The available entitites are: *corporation*, *location*, *person* and *date*.
+-   The app uses the [XLMRoberta model](https://huggingface.co/xlm-roberta-base), fine-tuned on the [wnut](https://huggingface.co/datasets/masakhane/masakhaner2) dataset.      
+-   The model uses the **byte-level BPE tokenizer**. Each sentece is first tokenized.  
        """
     )