Improve model card: Add pipeline tag, usage example, and project link

README.md

@@ -1,197 +1,113 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+pipeline_tag: text-classification
+tags:
+  - language-identification
+  - indian-languages
+  - multilingual
+  - muril
 ---
 
-# Model Card for Model ID
+# ILID: Native Script Language Identification for Indian Languages
 
-<!-- Provide a quick summary of what the model is/does. -->
+The model `yashingle-ai/ILID` is a MuRIL-based model fine-tuned for the language identification task, capable of identifying English and all 22 official Indian languages. It was presented in the paper [ILID: Native Script Language Identification for Indian Languages](https://huggingface.co/papers/2507.11832).
 
-The model is a MuRIL based finetuned model on the language identification task. The model can identify English and all 22 official Indian languaages.
+**Project Page:** [https://yashingle-ai.github.io/ILID/](https://yashingle-ai.github.io/ILID/)
+**Code Repository:** [https://github.com/yashingle-ai/TextLangDetect](https://github.com/yashingle-ai/TextLangDetect)
 
 ## Model Details
 
 ### Model Description
+This model is a fine-tuned version of `google/muril-base-cased` on the language identification task, capable of distinguishing between English and all 22 official Indian languages. It addresses the challenges of distinguishing languages in noisy, short, and code-mixed environments, particularly relevant for the diverse linguistic landscape of India.
 
-<!-- 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:** Pruthwik Mishra, Yash Ingle
-- **Funded by [optional]:** SVNIT, Surat
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** MIT
-- **Finetuned from model [optional]:** google/muril-base-cased
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [https://github.com/yashingle-ai/TextLangDetect]
-- **Paper [optional]:** [https://arxiv.org/abs/2507.11832]
-- **Demo [optional]:** [More Information Needed]
+-   **Developed by:** Pruthwik Mishra, Yash Ingle
+-   **Model type:** BERT-based for Sequence Classification
+-   **Language(s) (NLP):** Multilingual (22 official Indian languages + English)
+-   **Finetuned from model:** `google/muril-base-cased`
 
 ## Uses
 
-The model can be directly used for English and Indian language identification.
-### Direct Use
-
-The model can be directly used for English and Indian language identification.
-
-### Downstream Use [optional]
-
- Can be integrated into any pipeline that requires language identification for the concerned languages.
-
-[More Information Needed]
+The model can be directly used for English and Indian language identification, serving as a preprocessing step for applications like multilingual machine translation, information retrieval, and question answering.
 
 ### Out-of-Scope Use
-
-The model may not work for languages other than English and Indian languages.
-[More Information Needed]
+The model is not designed for languages other than English and the official Indian languages. Performance may vary on very low-resource Indian languages.
 
 ## Bias, Risks, and Limitations
-
-The model may not perform well on very resource poor languages such as Manipuri (in Meitei script), Sindhi, Maithili.
-[More Information Needed]
+The model may not perform optimally on very resource-poor languages such as Manipuri (in Meitei script), Sindhi, or Maithili, as highlighted in the original paper.
 
 ### 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 should be aware of the model's limitations and potential biases when applying it to specific use cases or languages outside its primary training scope.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+You can use the model directly with the `transformers` library for text classification:
 
-## Training Details
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-### Training Data
+# Load model and tokenizer
+model_name = "yashingle-ai/ILID" # Replace with actual model ID if different
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-<!-- 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. -->
+# Example usage (Hindi text)
+text_to_classify_hi = "\u0928\u092e\u0938\u094d\u0924\u0947, \u092f\u0939 \u090f\u0915 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0935\u093e\u0915\u094d\u092f \u0939\u0948\u0964"
+inputs_hi = tokenizer(text_to_classify_hi, return_tensors="pt")
 
-[More Information Needed]
+with torch.no_grad():
+    logits_hi = model(**inputs_hi).logits
 
-### Training Procedure
+predicted_class_id_hi = logits_hi.argmax().item()
+predicted_label_hi = model.config.id2label[predicted_class_id_hi] # Maps to LABEL_X
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+print(f"Text: '{text_to_classify_hi}'")
+print(f"Predicted language label: {predicted_label_hi}")
 
-#### Preprocessing [optional]
+# Example usage (English text)
+text_to_classify_en = "Hello, this is a test sentence."
+inputs_en = tokenizer(text_to_classify_en, return_tensors="pt")
 
-[More Information Needed]
+with torch.no_grad():
+    logits_en = model(**inputs_en).logits
 
+predicted_class_id_en = logits_en.argmax().item()
+predicted_label_en = model.config.id2label[predicted_class_id_en]
 
-#### Training Hyperparameters
+print(f"Text: '{text_to_classify_en}'")
+print(f"Predicted language label: {predicted_label_en}")
+```
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+## Training Details
 
-#### Speeds, Sizes, Times [optional]
+### Training Data
+The model was trained on the newly created ILID (Indian Language Identification Dataset).
 
-250K Dataset named ILID (Indian Language Identification Dataset)
-[More Information Needed]
+### Training Hyperparameters
+The model was fine-tuned from `google/muril-base-cased`.
 
 ## Evaluation
 
-The models are evaluated on the created corpora and the Bhasha-Abhijnaanam benchmark.
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The model was evaluated on the created ILID corpus and the Bhasha-Abhijnaanam benchmark.
 
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-F1-score
-[More Information Needed]
+### Metrics
+The primary evaluation metric used was F1-score.
 
 ### Results
+The model achieved an average F1-score of 0.96.
 
-0.96 F1 on an average
-#### 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:**
+## Citation
+If you find our work helpful or inspiring, please feel free to cite it.
 
+```bibtex
 @misc{ingle2025ilidnativescriptlanguage,
-      title={ILID: Native Script Language Identification for Indian Languages}, 
+      title={ILID: Native Script Language Identification for Indian Languages},
       author={Yash Ingle and Pruthwik Mishra},
       year={2025},
       eprint={2507.11832},
       archivePrefix={arXiv},
       primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2507.11832}, 
+      url={https://arxiv.org/abs/2507.11832},
 }
-**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]
+```