Update README.md

README.md

@@ -25,185 +25,226 @@ language:
 - vi
 - zh
 ---
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+# Model Checkpoints for Multilingual Machine-Generated Text Portion Detection
 
 ## Model Details
 
 ### Model Description
+- Developed by: 1-800-SHARED-TASKS
+- Funded by: Cohere's Research Compute Grant (July 2024)
+- Model type: Transformer-based for multilingual text portion detection
+- Languages (NLP): 23 languages (expanding to 102)
+- License: Non-commercial; derivatives must remain non-commercial with proper attribution
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Sources
+- **Code Repository:** [Github Placeholder]
+- **Paper:** [ACL Anthology Placeholder]
+- **Presentation:** [Multi-lingual Machine-Generated Text Portion(s) Detection](https://static1.squarespace.com/static/659ac5de66fdf20e1d607f2e/t/66d977a49597da76b6c260a1/1725527974250/MMGTD-Cohere.pdf)
 
 ## 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]
-
-### 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]
+The dataset is suitable for machine-generated text portion detection, token classification tasks, and other linguistic tasks. The methods applied here aim to improve the accuracy of detecting which portions of text are machine-generated, particularly in multilingual contexts. The dataset could be beneficial for research and development in areas like AI-generated text moderation, natural language processing, and understanding the integration of AI in content generation.
 
 ## 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]
+The model was trained on a dataset consisting of approximately 330k text samples from LLMs Command-R-Plus (100k) and Aya-23-35B (230k). The dataset includes 10k samples per language for each LLM, with a distribution of 10% fully human-written texts, 10% entirely machine-generated texts, and 80% mixed cases.
 
 ## 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]
+The model was evaluated on a multilingual dataset covering 23 languages. Metrics include Accuracy, Precision, Recall, and F1 Score at the word level (character level for Japanese and Chinese).
 
 ### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[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]
-
-## Model Card Contact
-
-[More Information Needed]
+Here are the word-level metrics for each language and ** character-level metrics for Japanese (JPN) and Chinese (ZHO):
+
+<table>
+  <tr>
+    <th>Language</th>
+    <th>Accuracy</th>
+    <th>Precision</th>
+    <th>Recall</th>
+    <th>F1 Score</th>
+  </tr>
+  <tr>
+    <td>ARA</td>
+    <td>0.923</td>
+    <td>0.832</td>
+    <td style="background-color: #e0e0e0;">0.992</td>
+    <td>0.905</td>
+  </tr>
+  <tr>
+    <td>CES</td>
+    <td>0.884</td>
+    <td>0.869</td>
+    <td style="background-color: #e0e0e0;">0.975</td>
+    <td>0.919</td>
+  </tr>
+  <tr>
+    <td>DEU</td>
+    <td>0.917</td>
+    <td>0.895</td>
+    <td style="background-color: #e0e0e0;">0.983</td>
+    <td>0.937</td>
+  </tr>
+  <tr>
+    <td>ELL</td>
+    <td>0.929</td>
+    <td>0.905</td>
+    <td style="background-color: #e0e0e0;">0.984</td>
+    <td>0.943</td>
+  </tr>
+  <tr>
+    <td>ENG</td>
+    <td>0.917</td>
+    <td>0.818</td>
+    <td style="background-color: #e0e0e0;">0.986</td>
+    <td>0.894</td>
+  </tr>
+  <tr>
+    <td>FRA</td>
+    <td>0.927</td>
+    <td>0.929</td>
+    <td style="background-color: #e0e0e0;">0.966</td>
+    <td>0.947</td>
+  </tr>
+  <tr>
+    <td>HEB</td>
+    <td>0.963</td>
+    <td>0.961</td>
+    <td style="background-color: #e0e0e0;">0.988</td>
+    <td>0.974</td>
+  </tr>
+  <tr>
+    <td>HIN</td>
+    <td>0.890</td>
+    <td>0.736</td>
+    <td style="background-color: #e0e0e0;">0.975</td>
+    <td>0.839</td>
+  </tr>
+  <tr>
+    <td>IND</td>
+    <td>0.861</td>
+    <td>0.794</td>
+    <td style="background-color: #e0e0e0;">0.988</td>
+    <td>0.881</td>
+  </tr>
+  <tr>
+    <td>ITA</td>
+    <td>0.941</td>
+    <td>0.906</td>
+    <td style="background-color: #e0e0e0;">0.989</td>
+    <td>0.946</td>
+  </tr>
+  <tr>
+    <td>JPN**</td>
+    <td>0.832</td>
+    <td>0.747</td>
+    <td style="background-color: #e0e0e0;">0.965</td>
+    <td>0.842</td>
+  </tr>
+  <tr>
+    <td>KOR</td>
+    <td>0.937</td>
+    <td>0.918</td>
+    <td style="background-color: #e0e0e0;">0.992</td>
+    <td>0.954</td>
+  </tr>
+  <tr>
+    <td>NLD</td>
+    <td>0.916</td>
+    <td>0.872</td>
+    <td style="background-color: #e0e0e0;">0.985</td>
+    <td>0.925</td>
+  </tr>
+  <tr>
+    <td>PES</td>
+    <td>0.822</td>
+    <td>0.668</td>
+    <td style="background-color: #e0e0e0;">0.972</td>
+    <td>0.792</td>
+  </tr>
+  <tr>
+    <td>POL</td>
+    <td>0.903</td>
+    <td>0.884</td>
+    <td style="background-color: #e0e0e0;">0.986</td>
+    <td>0.932</td>
+  </tr>
+  <tr>
+    <td>POR</td>
+    <td>0.805</td>
+    <td>0.679</td>
+    <td style="background-color: #e0e0e0;">0.987</td>
+    <td>0.804</td>
+  </tr>
+  <tr>
+    <td>RON</td>
+    <td>0.931</td>
+    <td>0.924</td>
+    <td style="background-color: #e0e0e0;">0.985</td>
+    <td>0.953</td>
+  </tr>
+  <tr>
+    <td>RUS</td>
+    <td>0.885</td>
+    <td>0.818</td>
+    <td style="background-color: #e0e0e0;">0.971</td>
+    <td>0.888</td>
+  </tr>
+  <tr>
+    <td>SPA</td>
+    <td>0.888</td>
+    <td>0.809</td>
+    <td style="background-color: #e0e0e0;">0.990</td>
+    <td>0.890</td>
+  </tr>
+  <tr>
+    <td>TUR</td>
+    <td>0.849</td>
+    <td>0.735</td>
+    <td style="background-color: #e0e0e0;">0.981</td>
+    <td>0.840</td>
+  </tr>
+  <tr>
+    <td>UKR</td>
+    <td>0.768</td>
+    <td>0.637</td>
+    <td style="background-color: #e0e0e0;">0.987</td>
+    <td>0.774</td>
+  </tr>
+  <tr>
+    <td>VIE</td>
+    <td>0.866</td>
+    <td>0.757</td>
+    <td style="background-color: #e0e0e0;">0.975</td>
+    <td>0.853</td>
+  </tr>
+  <tr>
+    <td>ZHO**</td>
+    <td>0.803</td>
+    <td>0.698</td>
+    <td style="background-color: #e0e0e0;">0.976</td>
+    <td>0.814</td>
+  </tr>
+</table>
+
+## **Authors**
+
+**Core Contributors**
+
+- Ram Kadiyala [[contact@rkadiyala.com](mailto:contact@rkadiyala.com)]
+- Siddartha Pullakhandam [[pullakh2@uwm.edu](mailto:pullakh2@uwm.edu)]
+- Kanwal Mehreen [[kanwal@traversaal.ai](mailto:kanwal@traversaal.ai)]
+- Ashay Srivastava [[ashays06@umd.edu](mailto:ashays06@umd.edu)]
+- Subhasya TippaReddy [[subhasyat@usf.edu](mailto:subhasyat@usf.edu)]
+
+
+**Extended Crew**
+- Arvind Reddy Bobbili [[abobbili@cougarnet.uh.edu](mailto:abobbili@cougarnet.uh.edu)]
+- Drishti Sharma [ ]
+- Suraj Chandrashekhar [[stelugar@umd.edu](mailto:stelugar@umd.edu)]
+- Modabbir Adeeb [[madeeb@umd.edu](mailto:madeeb@umd.edu)]
+- Srinadh Vura [ ]
+
+
+## **Contact**
+
+ [![Gmail](https://img.shields.io/badge/Gmail-D14836?style=for-the-badge&logo=gmail&logoColor=white)](mailto:contact@rkadiyala.com)