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 by adding VAD (Valence, Arousal, Dominance) annotations to committee sentences.
The VAD scores were generated using the VAD binomial regression models, which were developed specifically for VAD prediction, on embeddings produced by the 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
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}')