Update README.md (#1)

- Update README.md (a9202b413084cd223cf1c869d2dcf47330d4fcbb)

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- sinhala
+- bert
+- masked-language-model
+- sinhala-news
+license: apache-2.0
+language:
+- si
+metrics:
+- perplexity
+base_model:
+- Ransaka/sinhala-bert-medium-v2
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Sinhala-BERT Fine-Tuned MLM
 
+This model is a fine-tuned version of `Ransaka/sinhala-bert-medium-v2` on the Sinhala News Corpus dataset for Masked Language Modeling (MLM).
 
 ## 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.
+This Sinhala-BERT model was fine-tuned specifically for the Sinhala language to improve its capabilities in Masked Language Modeling. It leverages the architecture of BERT and was further optimized on the Sinhala News Corpus dataset, aiming to achieve better contextual language understanding for Sinhala text.
 
-- **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:** [Thilina Gunathilaka]
+- **Model type:** Transformer-based Language Model (BERT)
+- **Language(s) (NLP):** Sinhala (si)
+- **License:** Apache-2.0
+- **Finetuned from model [optional]:** [Ransaka/sinhala-bert-medium-v2](https://huggingface.co/Ransaka/sinhala-bert-medium-v2)
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Your Hugging Face Repository URL]
+- **Dataset:** [TestData-CrossLingualDocumentSimilarityMeasurement](https://github.com/UdeshAthukorala/TestData-CrossLingualDocumentSimilarityMeasurement)
 
 ## 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]
+This model can directly be used for:
+- Masked Language Modeling (filling missing words or predicting masked tokens)
+- Feature extraction for Sinhala text
 
 ### 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 fine-tuned further for various downstream NLP tasks in Sinhala, such as:
+- Text Classification
+- Named Entity Recognition (NER)
+- Sentiment Analysis
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- This model is specifically trained for Sinhala. Performance on other languages is likely poor.
+- Not suitable for tasks unrelated to textual data.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Like any language model, this model may inherit biases from its training data. It's recommended to assess model predictions for biases before deployment in critical applications.
 
 ### 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.
+- Evaluate model biases before deployment.
+- Ensure fair and transparent use of this model in sensitive contexts.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the code below to get started with this model:
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name")
+model = AutoModelForMaskedLM.from_pretrained("your-username/your-model-name")
+```
 
 ## 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 Sinhala News Corpus dataset, comprising Sinhala news articles.
 
 ### 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]
-
+- **Tokenization**: Sinhala-specific tokenization and text normalization
+- **Max Sequence Length**: 128
+- **MLM Probability**: 15%
 
 #### 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]
+- **Epochs:** 25
+- **Batch Size:** 2 (Gradient accumulation steps: 2)
+- **Optimizer:** AdamW
+- **Learning Rate:** 3e-5
+- **Mixed Precision:** FP32
 
 ## 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]
+Sinhala News Corpus dataset test split was used.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Perplexity:** Used to measure language modeling capability.
+- **Loss (Cross-Entropy):** Lower is better.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
+The final evaluation metrics obtained:
 
-## Model Examination [optional]
+| Metric        | Value |
+|---------------|-------|
+| Perplexity    | [15.95] |
+| Validation Loss | [2.77] |
 
-<!-- Relevant interpretability work for the model goes here -->
+#### Summary
 
-[More Information Needed]
+The model achieved strong MLM results on the Sinhala News Corpus dataset, demonstrating improved language understanding.
 
 ## 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).
+Carbon emissions were not explicitly tracked. For estimation, refer to [Machine Learning Impact calculator](https://mlco2.github.io/impact).
 
-- **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]
+- **Hardware Type:** GPU (Tesla T4)
+- **Hours used:** [Approximate training hours]
+- **Cloud Provider:** Kaggle
+- **Compute Region:** [Region used, e.g., us-central]
+- **Carbon Emitted:** [Estimated CO2 emissions]
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+Transformer-based BERT architecture optimized for Masked Language Modeling tasks.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
-
-[More Information Needed]
+- NVIDIA Tesla T4 GPU
 
 #### Software
-
-[More Information Needed]
+- Python 3.10
+- Transformers library by Hugging Face
+- PyTorch
 
 ## 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]
+If you use this model, please cite it as:
 
-[More Information Needed]
+```bibtex
+@misc{yourusername2024sinhalabert,
+  author = {Your Name},
+  title = {Sinhala-BERT Fine-Tuned on Sinhala News Corpus},
+  year = {2024},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Model Hub},
+  howpublished = {\url{https://huggingface.co/your-username/your-model-name}}
+}
+```
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
+- [Thilina Gunathilaka]