up example and pipeline usage

README.md

@@ -1,11 +1,17 @@
 ---
 language: 
   - pl
+
+pipeline_tag: text-classification
+
+widget:
+- text: "Cała ta śmieszna debata była próbą ukrycia problemów gospodarczych jakie są i nadejdą, pytania w większości o mało istotnych sprawach"
+  example_title: "example 1"
   
 tags:
   - text
   - sentiment
-  - political
+  - politics
 
 metrics:
   - accuracy 
@@ -31,15 +37,26 @@ model-index:
 This model is a fine-tuned version of [dkleczek/bert-base-polish-cased-v1](https://huggingface.co/dkleczek/bert-base-polish-cased-v1) to predict 3-categorical sentiment.
 Fine-tuned on 1k sample of manually annotated Twitter data.
 
+Mapping':
+id2label= {
+    0:'Negative', 
+    1:'Neutral', 
+    2:'Positive'
+}
+
+```
+from transformers import pipeline
 
+model_path = "eevvgg/PaReS-sentimenTw-political-PL"
+sentiment_task = pipeline(task = "sentiment-analysis", model = model_path, tokenizer = model_path)
 
-Mapping (id2label):
-mapping = {
-    0:'negative', 
-    1:'neutral', 
-    2:'positive'
-}
+sequence = ["Cała ta śmieszna debata była próbą ukrycia problemów gospodarczych jakie są i nadejdą, pytania w większości o mało istotnych sprawach", 
+            "Brawo panie ministrze!"]
+            
+result = sentiment_task(sequence)
+labels = [i['label'] for i in result] # ['Negative', 'Positive']            
 
+```
 
 
 ## Intended uses & limitations
@@ -49,22 +66,21 @@ Sentiment detection in Polish data (fine-tuned on tweets from political domain).
 
 ## Training and evaluation data
 
-Trained for 3 epochs, mini-batch size of 8.
-Training results: loss: 0.1358926964368792
+#### Trained for 3 epochs, mini-batch size of 8.
+#### Training results: loss: 0.1358926964368792
 
 
 ## Evaluation procedure
 
-
 It achieves the following results on the test set (10%):
 
-### Num examples = 100 
+#### Num examples = 100 
 
-### Batch size = 8 
+#### Batch size = 8 
 
-### Accuracy = 0.950 
+#### Accuracy = 0.950 
 
-### F1-macro = 0.944 
+#### F1-macro = 0.944 
 
               precision    recall  f1-score   support