Update README.md

README.md

@@ -1,13 +1,28 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- NLP
+- translation
+- Egyptian Arabic
+- English-Arabic
+license: apache-2.0
+datasets:
+- Omar-youssef/English-Egyptian-Arabic-Translation
+language:
+- en
+- ar
+metrics:
+- bleu
+base_model:
+- Helsinki-NLP/opus-mt-tc-big-en-ar
+pipeline_tag: translation
 ---
 
-# Model Card for Model ID
+# English to Egyptian Arabic Neural Machine Translation Model
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+This model translates English text into colloquial Egyptian Arabic. It is fine-tuned from the Helsinki-NLP/opus-mt-tc-big-en-ar base model on a specialized dataset of English-Egyptian Arabic translation pairs, ensuring natural, idiomatic, and culturally appropriate translations reflective of everyday Egyptian speech.
 
 ## Model Details
 
@@ -15,23 +30,12 @@ tags: []
 
 <!-- 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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+This is a specialized neural machine translation model that translates from English to Egyptian Arabic (Masri/Egyptian Colloquial Arabic). Unlike standard Arabic translation models that produce Modern Standard Arabic (MSA), this model is trained to generate authentic colloquial Egyptian Arabic with natural speech patterns, slang, and tone appropriate to contemporary Egyptian communication.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Model type:** Neural Machine Translation (Seq2Seq Transformer)
+- **Language(s) (NLP):** English (en), Egyptian Arabic (ar)
+- **License:** Apache 2.0
+- **Finetuned from model:** Helsinki-NLP/opus-mt-tc-big-en-ar
 
 ## Uses
 
@@ -39,66 +43,57 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### 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 is designed for translating English content into colloquial Egyptian Arabic. Suitable applications include:
+- Social media content localization
+- Dialogue systems and chatbots
+- Educational materials adaptation
+- Content creation for Egyptian audiences
+- Travel and hospitality applications
+- Business communication in Egyptian contexts
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
+- Formal or official document translation (prefer professional human translation or MSA models)
+- Legal or medical documents requiring precision and formal language
+- Highly technical or specialized terminology translation without domain-specific fine-tuning
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## How to Get Started with the Model
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Use the code below to get started with the model:
 
-## How to Get Started with the Model
+```python
+from transformers import MarianMTModel, MarianTokenizer
 
-Use the code below to get started with the model.
+model_name = "Omar-youssef/english-egyptian-arabic-translator"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
 
-[More Information Needed]
+# Example usage
+text = "Hello, how are you today?"
+inputs = tokenizer(text, return_tensors="pt")
+translated = model.generate(**inputs)
+output = tokenizer.decode(translated[0], skip_special_tokens=True)
+print(output)
+```
 
 ## 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. -->
+The model is trained on the **Omar-youssef/English-Egyptian-Arabic-Translation** dataset, specifically designed for English-to-Egyptian Arabic translation. The training data includes:
 
-[More Information Needed]
 
 ### 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]
+- **Training regime:** (bf16)
+- **Base Model:** Helsinki-NLP/opus-mt-tc-big-en-ar
+- **Learning Rate:** [5e-6]
+- **Batch Size:** [16]
+- **Epochs:** [5]
 
 ## Evaluation
 
@@ -106,94 +101,44 @@ Use the code below to get started with the model.
 
 ### 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
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+- **BLEU Score:** [0.4111]
 
-[More Information Needed]
 
-### Results
+## Technical Specifications
 
-[More Information Needed]
+### Model Architecture
 
-#### 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]
-
-### Model Architecture and Objective
-
-[More Information Needed]
+- **Architecture:** MarianMT (Encoder-Decoder Transformer)
+- **Base:** Helsinki-NLP/opus-mt-tc-big-en-ar
+- **Parameters:** ~500M
+- **Task:** Sequence-to-Sequence Translation
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
+- **Hardware:** Kaggle GPU (T4 )
+- **Framework:** Hugging Face Transformers 
+- **Training time:** ~60 minutes
 
-[More Information Needed]
-
-#### 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. -->
-
-**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]
+## Citation
 
-[More Information Needed]
+If you use this model, please cite the base model and dataset:
 
-## Model Card Authors [optional]
+```bibtex
+@misc{omar-youssef-egyptian-translator,
+  author    = {Omar Youssef},
+  title     = {English to Egyptian Arabic Translation Model},
+  year      = {2026},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/Omar-youssef/english-egyptian-arabic-translator}
+}
+```
 
-[More Information Needed]
+---
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or feedback, open a discussion on this model's [Hugging Face page](https://huggingface.co/Omar-youssef/english-egyptian-arabic-translator/discussions).