Model Card

Hi!👋
This PR has a some additional information for the model card, based on the format we are using as part of our effort to standardise model cards at Hugging Face. Feel free to merge if you are ok with the changes! (cc

@Marissa



@Meg

)

README.md

@@ -14,9 +14,113 @@ license: mit
 
 # hebrew-bad_wiki-gpt_neo-tiny
 
-Hebrew nonsense generation model which produces really bad wiki-abstract text. 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). Fine-tuning on the wiki-absract text was done using [@minimaxir](https://twitter.com/minimaxir)'s [aitextgen](https://github.com/minimaxir/aitextgen).
+## Table of Contents
+- [Model Details](#model-details)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Environmental Impact](#environmental-impact)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
 
-## Datasets
+## Model Details
+**Model Description:**
 
+The model developer notes that the model is 
+> Hebrew nonsense generation model which produces really bad wiki-abstract text. 
+
+
+- **Developed by:** [Doron Adler](https://github.com/Norod)
+- **Model Type:** Text Generation
+- **Language(s):** Hebrew
+- **License:** MIT
+- **Resources for more information:**
+- [GitHub Repo](https://github.com/Norod/hebrew-gpt_neo)
+- [HuggingFace Space](https://huggingface.co/spaces/Norod78/Hebrew-GPT-Neo-Small)
+
+
+## Uses
+
+#### Direct Use
+
+This model can be used for text generation.
+
+#### Misuse and Out-of-scope Use
+
+
+## Risks, Limitations and Biases
+**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
+
+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)).
+
+
+
+
+## Training
+
+
+#### Training Data
  [Hebrew Wikipedia Dump](https://dumps.wikimedia.org/hewiki/latest/) (hewiki abstract) from May 2020
 
+
+
+
+#### Training Procedure
+
+
+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). 
+
+Fine-tuning on the wiki-absract text was done using [@minimaxir](https://twitter.com/minimaxir)'s [aitextgen](https://github.com/minimaxir/aitextgen).
+
+
+
+## Evaluation
+
+
+#### Configs
+
+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) 
+
+* **Activation Function:** gelu
+* **Number_Head:** 12
+* **Number_Vocab:** 50257
+* **Train batch size:** 250
+* **Eval batch size:** 64
+* **Predict batch size:** 1
+
+
+
+
+## Environmental Impact
+
+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).
+
+
+- **Hardware Type:** [More information needed]
+
+- **Hours used:** Unknown
+
+- **Cloud Provider:** GCP  tpu-v8s
+
+- **Compute Region:** europe-west4
+
+- **Carbon Emitted:** [More information needed]
+
+
+## How to Get Started With the Model
+
+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)
+
+
+​​
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")
+
+model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")
+
+
+```
+
+