Update README.md

README.md

@@ -1,25 +1,53 @@
 ---
 license: apache-2.0
+language:
+- hu
+metrics:
+- accuracy
+model-index:
+- name: huBERTPlain
+  results:
+  - task:
+      type: text-classification
+    metrics:
+      - type: f1
+        value: 0.91
+widget:
+- text: "A vegetációs időben az országban rendszeresen jelentkező jégesők ellen is van mód védekezni lokálisan, ki-ki a saját nagy értékű ültetvényén."
+  example_title: "Positive"
+  
+- text: "Magyarország több évtizede küzd demográfiai válsággal, és egyre több gyermekre vágyó pár meddőségi problémákkal néz szembe."
+  exmaple_title: "Negative"
+
+- text: "Tisztelt fideszes, KDNP-s Képvi­selőtársaim!"
+  example_title: "Neutral"
+
 ---
 
 ## Model description
 
-Cased fine-tuned BERT model for Hungarian, trained on a dataset...
+Cased fine-tuned BERT model for Hungarian, trained on (manuallay anniated) parliamentary pre-agenda speeches scraped from `parlament.hu`. 
 
 ## Intended uses & limitations
 
-The model can be used as any other (cased) BERT model. It has been tested recognizing ..., where:
-* 
-* 
+The model can be used as any other (cased) BERT model. It has been tested recognizing positive, negative and neutral sentences in (parliamentary) pre-agenda speeches, where:
+* 'Label_0': Neutral
+* 'Label_1': Positive
+* 'Label_2': Negative
 
 ## Training
 
-Fine-tuned version of the original huBERT model (`SZTAKI-HLT/hubert-base-cc`), trained on ...
+Fine-tuned version of the original huBERT model (`SZTAKI-HLT/hubert-base-cc`), trained on HunEmPoli corpus.
 
 ## Eval results
 
 | Class | Precision | Recall | F-Score |
 |-----|------------|------------|------|
+|Neutral|0.83|0.71|0.76|
+|Positive|0.87|0.91|0.9|
+|Negative|0.94|0.91|0.93|
+|Macro AVG|0.88|0.85|0.86|
+|Weighted WVG|0.91|0.91|0.91|
 
 
 ## Usage
@@ -27,8 +55,8 @@ Fine-tuned version of the original huBERT model (`SZTAKI-HLT/hubert-base-cc`), t
 ```py
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-tokenizer = AutoTokenizer.from_pretrained("")
-model = AutoModelForSequenceClassification.from_pretrained("")
+tokenizer = AutoTokenizer.from_pretrained("poltextlab/HunEmBERT3")
+model = AutoModelForSequenceClassification.from_pretrained("poltextlab/HunEmBERT3")
 ```
 
 ### BibTeX entry and citation info