Add dataset card

README.md

@@ -1,71 +1,34 @@
 ---
-license: cc-by-4.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: train/data.jsonl
-  - split: test
-    path: test/test.jsonl
-task_categories:
-- text-classification
 language:
 - he
-size_categories:
-- 10K<n<100K
+task_categories:
+- text-classification
+license: other
+pretty_name: HebrewSentiment
+private: true
 ---
 
-# 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:
+# HebrewSentiment
 
-- **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]
+Hebrew sentiment analysis — Positive / Negative / Neutral
 
-## Models and Comparisons
+## Source
 
-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). 
+Originally sourced from the Hebrew NLP benchmark collection.
+Google Drive: https://drive.google.com/drive/folders/1nZVJGF29R_KQjbovOT79Ceai8NIaFZdQ
 
-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:
+## Files
 
-| 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 |
+- `data.jsonl`
+- `test_test.jsonl`
 
-## Contributors
+## Usage
 
-[To fill in]
+```python
+from datasets import load_dataset
+ds = load_dataset("HebArabNlpProject/HebrewSentiment", token=HF_TOKEN)
+```
 
-Contributors: [To fill in]
+## Task type
 
-## Acknowledgments
-We would like to express our gratitude to [To fill in]
+`classification`