Update README.md

README.md

@@ -1,199 +1,118 @@
 ---
 library_name: transformers
-tags: []
+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
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# 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
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+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.
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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](https://huggingface.co/hooking-dev/Hebrew_v1.0)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+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
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+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
 
-<!-- This section is meant to convey both technical and sociotechnical 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.
 
-[More Information Needed]
-
-### Recommendations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+To get started with HebrewGPT_Base_v1.0, load the model using the Transformers library:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-## How to Get Started with the Model
+model = GPT2LMHeadModel.from_pretrained("hooking-dev/Hebrew_v1.0")
+tokenizer = GPT2Tokenizer.from_pretrained("hooking-dev/Hebrew_v1.0")
 
-Use the code below to get started with the model.
+# Example text
+input_ids = tokenizer.encode("שלום, מה שלומך?", return_tensors="pt")
 
-[More Information Needed]
+# Generate text
+outputs = model.generate(input_ids)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+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.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Optimizer:** AdamW
+- **Learning Rate:** 0.0002
+- **Training Epochs:** 2
+- **Batch Size:** 16
+- **Sequence Length:** 512
+- **Warmup Steps:** 500
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### 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.
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model uses a standard GPT architecture with 16 transformer layers, 16 attention heads, and a hidden size of 1024.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
+Training was conducted on GPU-accelerated hardware, specifically using NVIDIA Tesla GPUs.
 
-[More Information Needed]
+## Citation
 
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+If you use this model in your research, please cite it as follows:
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```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}
+}