Update README.md

README.md

@@ -1,201 +1,70 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- NLP
+- Machine Translation
+- Moroccan Arabic
+- Darija
+- Modern Standard Arabic
+- MSA
+- AraT5
+pipeline_tag: translation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for AraT5 - Moroccan Arabic to Modern Standard Arabic Translation
 
 ## 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]
+This model card presents a 🤗 transformers model designed for translating Moroccan Arabic (Darija) into Modern Standard Arabic (MSA). The model is fine-tuned from AraT5 base 1024.
 
-### Model Sources [optional]
+- **Developed by:** Said ET-TOUSY.
+- **Model type:** Fine-tuned language translation model
+- **Language(s) (NLP):** Moroccan Arabic (Darija), Modern Standard Arabic (MSA)
+- **Finetuned from model :** AraT5 base 1024
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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 model is intended to be used directly for translating text from Moroccan Arabic (Darija) to Modern Standard Arabic (MSA). It can be deployed in various applications requiring translation services.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use 
 
-[More Information Needed]
+The model can also be fine-tuned for specific downstream tasks related to Moroccan Arabic and Modern Standard Arabic. This could include domain-specific translations or integration into larger NLP systems.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+While the model is designed for translation between Moroccan Arabic and Modern Standard Arabic, it may not perform well on other language pairs or tasks unrelated to translation.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model's performance may be influenced by biases present in the training data, such as the representation of certain dialectal variations or cultural nuances. Additionally, the model's accuracy may vary depending on the complexity of the text being translated and the presence of out-of-vocabulary words.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. Careful evaluation of translated outputs, especially in sensitive or critical applications, is recommended. Furthermore, continuous monitoring and updating of the model with new data can help mitigate biases and improve performance over time.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## 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]
-
-### 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]
-
-## 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
-
-<!-- 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]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[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]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+To get started with the model, follow the steps below:
 
-## Model Card Contact
+1. Install the transformers library.
+2. Load the pre-trained AraT5_Darija_to_MSA model fine-tuned for Moroccan Arabic to Modern Standard Arabic translation.
+3. Use the model to translate text from Moroccan Arabic to Modern Standard Arabic.
 
-[More Information Needed]
+```python
+# Example code
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
+model_name = "Saaidtaoussi/AraT5_Darija_to_MSA"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
+# Example translation
+input_text = "أه, يالاه رجاعت غير من شهار لعاسال ديالي ف شفشاون"
+inputs = tokenizer(input_text, return_tensors="pt", padding=True)
+translated = model.generate(**inputs)
+output_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+print(output_text)
+# نعم، لقد عدت للتو من شهر العسل في شفشاون