HebArabNlpProject/HebrewSentiment
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How to use dicta-il/dictabert-sentiment with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="dicta-il/dictabert-sentiment") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("dicta-il/dictabert-sentiment", device_map="auto")State-of-the-art language model for Hebrew.
This is the fine-tuned BERT-base model for the sentiment-analysis task on the HebrewSetniment dataset release by the Israeli National Program for Hebrew and Arabic NLP.
For the bert-base models for other tasks, see here.
Sample usage:
from transformers import pipeline
oracle = pipeline('sentiment-analysis', model='dicta-il/dictabert-sentiment')
sentence = '''אני מאוד שמח שהמודל הזה משוחרר לשימוש חופשי'''
oracle(sentence)
Output:
[
{
"label": "Positive",
"score": 0.9999868869781494
}
]
If you use DictaBERT in your research, please cite DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew
BibTeX:
@misc{shmidman2023dictabert,
title={DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew},
author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel},
year={2023},
eprint={2308.16687},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
This work is licensed under a Creative Commons Attribution 4.0 International License.