update readme 1

README.md

@@ -1,3 +1,30 @@
+---
+language: 
+  - pl
+  
+tags:
+  - text
+  - sentiment
+  - political
+
+metrics:
+  - accuracy 
+  - f1
+
+model-index:
+- name: PaReS-sentimenTw-political-PL
+  results:
+  - task:
+      type: sentiment-classification             # Required. Example: automatic-speech-recognition
+      name: Text Classification            # Optional. Example: Speech Recognition
+    dataset:
+      type: tweets          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
+      name: tweets_2020_electionsPL          # Required. A pretty name for the dataset. Example: Common Voice (French)
+    metrics:
+      - type: f1          # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 94.4       # Required. Example: 20.90
+
+---
 
 # PaReS-sentimenTw-political-PL
 
@@ -5,6 +32,7 @@ This model is a fine-tuned version of [dkleczek/bert-base-polish-cased-v1](https
 Fine-tuned on 1k sample of manually annotated Twitter data.
 
 
+
 Mapping (id2label):
 mapping = {
     0:'negative', 
@@ -30,10 +58,10 @@ Training results: loss: 0.1358926964368792
 
 It achieves the following results on the test set (10%):
 
-  Num examples = 100
-  Batch size = 8
-  Accuracy = 0.950
-  F1-macro = 0.944
+  Num examples = 100 \n
+  Batch size = 8 \n
+  Accuracy = 0.950 \n
+  F1-macro = 0.944 \n
 
               precision    recall  f1-score   support
 
@@ -41,7 +69,4 @@ It achieves the following results on the test set (10%):
            1      0.958     0.885     0.920        26
            2      0.923     0.960     0.941        25
 
-    accuracy                          0.950       100
-   macro avg      0.947     0.941     0.944       100
-weighted avg      0.950     0.950     0.950       100