metadata

library_name: transformers
tags:
  - gpt
  - hebrew
  - language-model
  - pretraining
license: apache-2.0
datasets:
  - oscar-corpus/OSCAR-2301
metrics:
  - perplexity
model-index:
  - name: HebrewGPT_Base_v1.0
    results:
      - task:
          name: Language Modeling
          type: language-modeling
        dataset:
          name: OSCAR Hebrew
          type: oscar-corpus/OSCAR-2301
        metrics:
          - name: Perplexity
            type: perplexity
            value: More Information Needed

HebrewGPT_Base_v1.0

This is the HebrewGPT_Base_v1.0 model, a foundational GPT model for the Hebrew language, pretrained from scratch on the OSCAR Hebrew dataset.

Model Details

Model Description

Developed by Hooking AI, this model is the base version of a Hebrew GPT series intended for further fine-tuning and downstream NLP tasks in Hebrew. It serves as a generic foundation for Hebrew language understanding and generation.

Developed by: Hooking AI
Model type: GPT (Generative Pre-trained Transformer)
Language(s) (NLP): Hebrew
License: Apache-2.0
Repository: hooking-dev/Hebrew_v1.0

Uses

Direct Use

This model can be used directly for tasks that involve understanding or generating Hebrew text, such as conversation modeling, text summarization, and more. It has not been fine-tuned on any downstream tasks and is best suited as a starting point for further NLP applications.

Out-of-Scope Use

The model is not recommended for use in high-stakes scenarios such as medical diagnosis or legal decision-making due to the lack of domain-specific fine-tuning and potential biases inherent in language models.

Bias, Risks, and Limitations

The model, like many language models, likely contains biases that are present in the training data. Users should be aware of these potential biases when using the model, especially in sensitive applications. Further research and auditing for bias are recommended before deploying the model in production.

How to Get Started with the Model

To get started with HebrewGPT_Base_v1.0, load the model using the Transformers library:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("hooking-dev/Hebrew_v1.0")
tokenizer = GPT2Tokenizer.from_pretrained("hooking-dev/Hebrew_v1.0")

# Example text
input_ids = tokenizer.encode("שלום, מה שלומך?", return_tensors="pt")

# Generate text
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was trained on the OSCAR Hebrew dataset, a large-scale, open corpus consisting of diverse text collected from the web, reflecting common usage of Hebrew in various contexts. For more details on the dataset, see the citations related to OSCAR below.

Training Procedure

Training Hyperparameters

Optimizer: AdamW
Learning Rate: 0.0002
Training Epochs: 2
Batch Size: 16
Sequence Length: 512
Warmup Steps: 500

Evaluation

Testing Data, Factors & Metrics

Since this model is a base model and not fine-tuned on specific downstream tasks, standard language modeling metrics such as perplexity were primarily considered during development. Detailed evaluation results will be added as further testing is conducted.

Technical Specifications

Model Architecture and Objective

The model uses a standard GPT architecture with 16 transformer layers, 16 attention heads, and a hidden size of 1024.

Compute Infrastructure

Training was conducted on GPU-accelerated hardware, specifically using NVIDIA Tesla GPUs.

Citation

If you use this model in your research, please cite it as follows:

BibTeX:

@misc{hebrewgpt_base_v1_0,
  title={HebrewGPT Base Model},
  author={Hooking AI},
  howpublished={Hugging Face Model Hub},
  year={2024},
  url={https://huggingface.co/hooking-dev/Hebrew_v1.0}
}

@article{2022arXiv221210440J,
  author = {{Jansen}, Tim and {Tong}, Yangling and {Zevallos}, Victoria and {Ortiz Suarez}, Pedro},
  title = "{Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data}",
  journal = {arXiv e-prints},
  year = 2022,
  month = dec,
  eid = {arXiv:2212.10440},
  pages = {arXiv:2212.10440},
  doi = {10.48550/arXiv.2212.10440},
  archivePrefix = {arXiv},
  eprint = {2212.10440},
  primaryClass = {cs.CL},
  adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv221210440J},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

}