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---
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}')
```
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