README.md

---
configs:
- config_name: default
  data_files:
  - split: train
    path: "knesset_committees_sentences_with_vad_shards.tar"
license: cc-by-4.0
datasets:
- HaifaCLGroup/KnessetCorpus
task_categories:
- text-classification
language:
- he
viewer: false

size_categories:
- 10M<n<100M
---
# VAD_KnessetCorpus

  This dataset extends the original [Knesset Corpus](https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus)
  by adding VAD (Valence, Arousal, Dominance) annotations to committee sentences.
  
  The VAD scores were generated using the [VAD binomial regression models](https://huggingface.co/GiliGold/VAD_binomial_regression_models), which were developed specifically for VAD prediction, on embeddings produced by 
  the [Knesset-multi-e5-large](https://huggingface.co/GiliGold/Knesset-multi-e5-large).
  
  Additionally, a small subset of 120 Knesset sentences has been manually annotated by three annotators for VAD scores, and the file is available in the repository.
  


## VAD_scores:
- **Valence** (or pleasure) measures the positivity or negativity of an emotion on a scale from 0 to 1.

- **Arousal** quantifies the intensity of an emotion, from calm to excited on a scale from 0 to 1.

- **Dominance** represents the degree of control conveyed by a word, from submissive to dominant on a scale from 0 to 1.

## Usage
  
  After dowloading and extracting jsonl file from here: https://huggingface.co/datasets/GiliGold/VAD_KnessetCorpus/tree/main
  ```python
  vad_shard_path = "women_rights_vad_shards_bzip2_files\vad_shard_0.jsonl" #adjust path accordingly
  
  with open(vad_shard_path, encoding="utf-8") as vad_file:
    for line in vad_file:
        sentence_entity = json.loads(line)
        sentence_text = sentence_entity["sentence_text"]
        v = sentence_entity["vad_values"]["valence"]
        a = sentence_entity["vad_values"]["arousal"]
        d = sentence_entity["vad_values"]["dominance"]
        print(f'sentence text: {sentence_text}')
        print(f'valence value is: {v}')
  ```