Model Card (#1)
Browse files- Model Card (425a0dd4ea29d4c1116b93d2f05de9d5355ee55a)
Co-authored-by: Ezi Ozoani <Ezi@users.noreply.huggingface.co>
README.md
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# hebrew-bad_wiki-gpt_neo-tiny
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##
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[Hebrew Wikipedia Dump](https://dumps.wikimedia.org/hewiki/latest/) (hewiki abstract) from May 2020
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# hebrew-bad_wiki-gpt_neo-tiny
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## Table of Contents
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- [Model Details](#model-details)
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- [Uses](#uses)
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- [Risks, Limitations and Biases](#risks-limitations-and-biases)
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- [Training](#training)
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- [Evaluation](#evaluation)
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- [Environmental Impact](#environmental-impact)
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- [How to Get Started With the Model](#how-to-get-started-with-the-model)
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## Model Details
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**Model Description:**
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The model developer notes that the model is
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> Hebrew nonsense generation model which produces really bad wiki-abstract text.
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- **Developed by:** [Doron Adler](https://github.com/Norod)
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- **Model Type:** Text Generation
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- **Language(s):** Hebrew
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- **License:** MIT
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- **Resources for more information:**
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- [GitHub Repo](https://github.com/Norod/hebrew-gpt_neo)
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- [HuggingFace Space](https://huggingface.co/spaces/Norod78/Hebrew-GPT-Neo-Small)
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## Uses
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#### Direct Use
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This model can be used for text generation.
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#### Misuse and Out-of-scope Use
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## Risks, Limitations and Biases
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**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
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## Training
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#### Training Data
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[Hebrew Wikipedia Dump](https://dumps.wikimedia.org/hewiki/latest/) (hewiki abstract) from May 2020
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#### Training Procedure
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This model was fined tuned upon [hebrew-gpt_neo-tiny](https://huggingface.co/Norod78/hebrew-gpt_neo-tiny) which was previously trained using [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo).
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Fine-tuning on the wiki-absract text was done using [@minimaxir](https://twitter.com/minimaxir)'s [aitextgen](https://github.com/minimaxir/aitextgen).
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## Evaluation
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#### Configs
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Model configs for the hebrew-gpt_neo-tiny is available on the [hebrew-gpt_neo model github](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-tiny/configs)
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* **Activation Function:** gelu
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* **Number_Head:** 12
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* **Number_Vocab:** 50257
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* **Train batch size:** 250
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* **Eval batch size:** 64
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* **Predict batch size:** 1
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type based on the [associated paper](https://arxiv.org/pdf/2105.09680.pdf).
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- **Hardware Type:** [More information needed]
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- **Hours used:** Unknown
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- **Cloud Provider:** GCP tpu-v8s
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- **Compute Region:** europe-west4
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- **Carbon Emitted:** [More information needed]
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## How to Get Started With the Model
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A Google Colab Notebook is also available [here](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-tiny/Norod78_hebrew_gpt_neo_tiny_Colab.ipynb)
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")
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model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")
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```
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