Update README.md

README.md

@@ -1,25 +1,52 @@
 ---
 license: apache-2.0
+language:
+- hu
+metrics:
+- accuracy
+model-index:
+- name: huBERTPlain
+  results:
+  - task:
+      type: text-classification
+    metrics:
+      - type: f1
+        value: 0.77
 ---
 
 ## 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 emotions at the sentence level in (parliamentary) pre-agenda speeches, where:
+* 'Label_0': Neutral
+* 'Label_1': Fear
+* 'Label_3': Sadness
+* 'Label_4': Anger
+* 'Label_5': Disgust
+* 'Label_6': Success
+* 'Label_7': Joy
 
 ## 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 |
 |-----|------------|------------|------|
+| Fear    | 0.625  | 0.625  | 0.625  |
+| Sadness | 0.8535 | 0.6291 | 0.7243 |
+| Anger   | 0.7857 | 0.3437 | 0.4782 |
+| Disgust | 0.7154 | 0.8790 | 0.7888 |
+| Success | 0.8579 | 0.8683 | 0.8631 |
+| Joy     | 0.549  | 0.6363 | 0.5894 |
+| Trust        | 0.4705 | 0.5581 | 0.5106 |
+| ------------ | ------ | ------ | ------ |
+| Macro AVG    | 0.7134 | 0.6281 | 0.6497 |
+| Weighted AVG | 0.791  | 0.7791 | 0.7743 |
 
 
 ## Usage
@@ -27,8 +54,8 @@ 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/HunEmBERT8")
+model = AutoModelForSequenceClassification.from_pretrained("poltextlab/HunEmBERT8")
 ```
 
 ### BibTeX entry and citation info