Update README.md

README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+----
 
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+* transformers
+* pytorch
+* text-classification
+* news-classification
+* ag-news 
+* nlp
+----
 
-## Model Details
+# News Classification using RoBERTa
 
-### Model Description
+## Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is a fine-tuned RoBERTa model for multiclass news classification. It predicts the category of a news headline or article among four classes:
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+* World
+* Sports
+* Business
+* Sci/Tech
 
-- **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]
+The model was fine-tuned on the AG News dataset using Hugging Face Transformers and PyTorch.
 
-### Model Sources [optional]
+### Model Details
 
-<!-- Provide the basic links for the model. -->
+* **Developed by:** MhoOmm
+* **Model Type:** RoBERTa for Sequence Classification
+* **Language:** English
+* **License:** MIT
+* **Finetuned From:** roberta-base
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Labels
 
-## Uses
+| Label ID | Category |
+| -------- | -------- |
+| 0        | World    |
+| 1        | Sports   |
+| 2        | Business |
+| 3        | Sci/Tech |
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Intended Use
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be used for:
 
-[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]
+* News article categorization
+* Content organization systems
+* News recommendation pipelines
+* NLP learning projects
+* Text classification demonstrations
 
 ### 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 model is not intended for:
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+* Medical diagnosis
+* Financial advice
+* Legal decision making
+* Safety-critical systems
+* Classification of non-English news
 
-[More Information Needed]
+## Usage
 
-### Recommendations
+```python
+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+import torch
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+model_name = "MhoOmm/news-classifier"
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-## How to Get Started with the Model
+text = "Apple launches a new AI-powered processor"
 
-Use the code below to get started with the model.
+inputs = tokenizer(
+    text,
+    return_tensors="pt",
+    truncation=True
+)
 
-[More Information Needed]
+with torch.no_grad():
+    outputs = model(**inputs)
 
-## Training Details
+prediction = outputs.logits.argmax(-1).item()
 
-### Training Data
+print(model.config.id2label[prediction])
+```
 
-<!-- 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. -->
+## Training Data
 
-[More Information Needed]
+The model was fine-tuned on the AG News dataset.
 
-### Training Procedure
+### Dataset Statistics
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+* Training Samples: 120,000
+* Test Samples: 7,600
+* Classes: 4
 
-#### Preprocessing [optional]
+### Categories
 
-[More Information Needed]
+* World
+* Sports
+* Business
+* Sci/Tech
 
+## Training Procedure
 
-#### Training Hyperparameters
+### Preprocessing
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+* RoBERTa tokenizer
+* Maximum sequence length: 128
+* Truncation enabled
+* Dynamic padding
 
-#### Speeds, Sizes, Times [optional]
+### Hyperparameters
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+* Learning Rate: 2e-5
+* Batch Size: 16
+* Epochs: 3
+* Optimizer: AdamW
+* Framework: PyTorch
+* Trainer: Hugging Face Trainer
 
 ## 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]
+### Metrics
 
-#### Factors
+The model was evaluated using classification accuracy.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### Example Predictions
 
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+| Input                                         | Prediction |
+| --------------------------------------------- | ---------- |
+| India defeats Australia in the cricket final  | Sports     |
+| Stock market reaches a new all-time high      | Business   |
+| Apple unveils a new AI-powered device         | Sci/Tech   |
+| Global leaders meet to discuss climate policy | World      |
 
 ### 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]
+* Accuracy: **94.76%**
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Limitations
 
-**BibTeX:**
+* Trained only on English-language news articles.
+* May perform poorly on informal text such as tweets and social media posts.
+* Restricted to the four AG News categories.
+* Performance may decrease on domains significantly different from the training data.
 
-[More Information Needed]
+## Architecture
 
-**APA:**
+* Base Model: RoBERTa Base
+* Hidden Size: 768
+* Transformer Layers: 12
+* Attention Heads: 12
+* Classification Head: Linear Layer (768 → 4)
 
-[More Information Needed]
+## Software
 
-## Glossary [optional]
+* PyTorch
+* Transformers
+* Hugging Face Hub
+* Gradio
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Author
 
-[More Information Needed]
+Created by **MhoOmm** as an end-to-end NLP project demonstrating:
 
-## More Information [optional]
+* Transformer fine-tuning
+* Text classification
+* Model deployment with Hugging Face
+* Interactive inference using Gradio
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model, please cite the original RoBERTa paper:
 
-[More Information Needed]
+**RoBERTa: A Robustly Optimized BERT Pretraining Approach**
 
-## Model Card Contact
+Yinhan Liu, Myle Ott, Naman Goyal, et al.
 
-[More Information Needed]
+https://arxiv.org/abs/1907.11692