Update README.md

README.md

@@ -1,10 +1,71 @@
 ---
 license: cc-by-4.0
 configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: train/data.jsonl
-      - split: test
-        path: test/data.jsonl
+- config_name: default
+  data_files:
+  - split: train
+    path: train/data.jsonl
+  - split: test
+    path: test/data.jsonl
+task_categories:
+- text-classification
+language:
+- he
+size_categories:
+- 10K<n<100K
 ---
+
+# HebrewSentiment - A Sentiment-Analysis Dataset in Hebrew
+
+## Summary
+HebrewSentiment is a Hebrew dataset for the sentiment analysis task. 
+
+## Introduction
+
+This dataset was constructed via [To Fill In].
+
+## Dataset Statistics
+
+The table below shows the number of examples from each category in each of the splits:
+
+| split |  total   | positive | negative | neutral |
+|-------|----------|----------|----------|---------|
+| train |  39,135  |   8,968  | 7,669    | 22,498  |
+| test  |   2,170  |   503    |   433    | 1,234   |
+
+## Dataset Description
+
+Each row in the dataset contains the following fields:
+
+- **id**: A unique identifier for that training examples
+- **text**: The textual content of the input sentence
+- **tag_ids**: The label of the example (`Neutral`/`Positive`/`Negative`)
+- **task_name**: [To fill in]
+- **campaign_id**: [To fill in]
+- **annotator_agreement_strength**: [To fill in]
+- **survey_name**: [To fill in]
+- **industry**: [To fill in]
+- **type**: [To fill in]
+
+## Models and Comparisons
+
+In collaboration with [DICTA](https://dicta.org.il/) we trained a model on this dataset and are happy to release it to the public: [DictaBERT-Sentiment](https://huggingface.co/dicta-il/dictabert-sentiment). 
+
+In addition, we compared the performance of the model to the previous existing sentiment dataset - [Hebrew-Sentiment-Data from OnlpLab](https://github.com/OnlpLab/Hebrew-Sentiment-Data). 
+We fine-tuned [dictabert](https://huggingface.co/dicta-il/dictabert) 3 times - once on the OnlpLab dataset, once on this dataset, and once on both datasets together and the results can be seen in the table below:
+
+| Training Corpus: |      OnlpLab    |    |      |      |        | HebrewSentiment|      |      |   |    |
+|------------------|------|----------------|------|------|--------|--------------|------|------|---|---|
+|                  | Accuracy  | Macro F1       | F1 Positive  | F1 Negative | F1 Neutral   | Accuracy    | Macro F1 | F1 Positive   | F1 Negative  | F1 Neutral |
+| OnlpLab+HebrewSentiment       | 87   | 61.7           | 93.2 | 74.6 | 17.4 | 83.9           | 82.7     | 79.8 | 81.8 | 86.4 |
+| OnlpLab             | 88.2 | 63.3           | 93.8 | 72.1 | 24   | 41.3           | 42.2     | 48.1 | 56.3 | 22.2 |
+| HebrewSentiment            | 69.9 | 51.7           | 82.2 | 62.9 | 10.2 | 84.4           | 83.2     | 81   | 82.1 | 86.6 |
+
+## Contributors
+
+[To fill in]
+
+Contributors: [To fill in]
+
+## Acknowledgments
+We would like to express our gratitude to [To fill in]