Update README.md

README.md

@@ -3,6 +3,7 @@ license: cc-by-4.0
 datasets:
 - dsfsi/vukuzenzele-monolingual
 - nchlt
+- dsfsi/PuoData
 language:
 - tn
 library_name: transformers
@@ -11,153 +12,46 @@ tags:
 - masked langauge model
 - setswana
 ---
-# Model Card for PuoBERTa
+# PuoBerta: A curated Setswana Language Model
 
+A Roberta-based language model specially designed for Setswana, using the new PuoData dataset.
 
 ## Model Details
 
+
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This is a masked language model trained on Setswana corpora, making it a valuable tool for a range of downstream applications from translation to content creation. It's powered by the PuoData dataset to ensure accuracy and cultural relevance.
 
 - **Developed by:** Vukosi Marivate ([@vukosi](https://huggingface.co/@vukosi)), Moseli Mots'Oehli ([@MoseliMotsoehli](https://huggingface.co/@MoseliMotsoehli)) , Valencia Wagner, Richard Lastrucci and Isheanesu Dzingirai
 - **Model type:** RoBERTa Model
 - **Language(s) (NLP):** Setswana
 - **License:** CC BY 4.0
 
-<!--  ### Model Sources [optional] -->
 
-<!-- Provide the basic links for the model. -->
+### Usage
 
-<!--- **Repository:** [More Information Needed] . -->
-<!-- - **Paper [optional]:** [More Information Needed] . -->
-<!-- - **Demo [optional]:** [More Information Needed] . -->
+Use this model filling in masks or finetune for downstream tasks. Here’s a simple example for masked prediction:
 
-## Uses
+```python
+from transformers import RobertaTokenizer, RobertaModel
 
-Pre-trained masked language model for Setswana. Model can be fine-tuned for downstream NLP tasks for Setswana. 
+# Load model and tokenizer
+model = RobertaModel.from_pretrained('dsfsi/PuoBERTa')
+tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTa')
 
+```
+ 
 ### Downstream Use 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-<!-- ### 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
-
-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. -->
-
-<!-- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-<!--  ## 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 Data 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
+## Dataset
 
-<!-- This should link to a Data 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 -->
-
-
-<!-- R### 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] -->
+We used the PuoData dataset, a rich source of Setswana text, ensuring that our model is well-trained and culturally attuned.\\
 
+## Contributing
 
+Your contributions are welcome! Feel free to improve the model.
 
 ## Model Card Authors
 
@@ -165,4 +59,8 @@ Vukosi Marivate
 
 ## Model Card Contact
 
-vukosi.marivate@cs.up.ac.za
+For more details, reach out or check our [website](https://dsfsi.github.io/).
+
+Email: [email protected]
+
+**Enjoy exploring Setswana through AI!**