Update README.md

README.md

@@ -1,199 +1,138 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- topic
+- multi-sentiment
+license: mit
+datasets:
+- valurank/Topic_Classification
+language:
+- en
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model:
+- distilbert/distilbert-base-uncased
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Topic Classification Model
 
+A fine-tuned DistilBERT model for multi-class topic classification. This model predicts the most relevant topic label from a predefined set based on input text. It was trained using 🤗 Transformers and PyTorch on a custom dataset derived from academic and news-style corpora.
 
 ## 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.
-
-- **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]
+This model was developed by Daniel (@AfroLogicInsect) to classify text into one of several predefined topics. It builds on the `distilbert-base-uncased` architecture and was fine-tuned for multi-class classification using a softmax output layer.
 
-### Model Sources [optional]
+- **Developed by:** Daniel 🇳🇬 (@AfroLogicInsect)
+- **Model type:** DistilBERT-based multi-class sequence classifier
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from:** distilbert-base-uncased
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [AfroLogicInsect/topic-model-analysis-model](https://huggingface.co/AfroLogicInsect/topic-model-analysis-model)
+- **Paper:** arXiv:1910.09700 (DistilBERT)
+- **Demo:** [Coming soon]
 
 ## 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]
+- Classify academic or news-style text into topics such as AI, finance, sports, climate, etc.
+- Embed in dashboards or content moderation tools for automatic tagging
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+- Can be extended to hierarchical topic classification
+- Useful for building recommendation engines or content filters
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not suitable for sentiment or emotion classification
+- May not generalize well to informal or slang-heavy text
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- Trained on curated corpora — may reflect biases in source material
+- Topics are predefined and static — emerging topics may be misclassified
+- Confidence scores are probabilistic, not definitive
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Use `top_k=5` with `return_all_scores=True` to retrieve multiple topic predictions
+- Consider fine-tuning on domain-specific data for improved accuracy
 
-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
 
-## How to Get Started with the Model
+```python
+from transformers import pipeline
 
-Use the code below to get started with the model.
+classifier = pipeline(
+    "text-classification",
+    model="AfroLogicInsect/topic-model-analysis-model",
+    tokenizer="AfroLogicInsect/topic-model-analysis-model",
+    return_all_scores=True
+)
 
-[More Information Needed]
+text = "New AI breakthrough in natural language processing"
+results = classifier(text)
+top_5 = sorted(results[0], key=lambda x: x['score'], reverse=True)[:5]
+for i, res in enumerate(top_5):
+    print(f"Top {i+1}: {res['label']} ({res['score']:.3f})")
+```
 
 ## 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]
+### Dataset
 
-[More Information Needed]
+- Custom multi-class topic dataset based on arXiv abstracts and news articles
+- Labels include domains like AI, finance, sports, climate, etc.
 
+### Hyperparameters
 
-#### Training Hyperparameters
+- Epochs: 3
+- Batch size: 16
+- Learning rate: 2e-5
+- Evaluation every 200 steps
+- Metric: F1 score
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Trainer Setup
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+Used Hugging Face `Trainer` API with `TrainingArguments` configured for early stopping and best model selection.
 
 ## 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]
-
-### Results
-
-[More Information Needed]
+Model achieved strong performance across multiple topic categories. Evaluation metrics include:
 
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+- **Accuracy:** ~90.8%
+- **F1 Score:** ~0.91
+- **Precision:** ~0.89
+- **Recall:** ~0.93
 
 ## 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:**
-
-[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]
+- **Hardware:** Google Colab (NVIDIA T4 GPU)
+- **Training Time:** ~2.5 hours
+- **Carbon Emitted:** ~0.3 kg CO₂eq (estimated via [ML Impact Calculator](https://mlco2.github.io/impact#compute))
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+```bibtex
+@misc{afrologicinsect2025topicmodel,
+  title = {AfroLogicInsect Topic Classification Model},
+  author = {Akan Daniel},
+  year = {2025},
+  howpublished = {\url{https://huggingface.co/AfroLogicInsect/topic-model-analysis-model}},
+}
+```
 
-## Model Card Contact
+## Contact
 
-[More Information Needed]
+- Name: Daniel (@AfroLogicInsect)
+- Location: Lagos, Nigeria
+- Contact: GitHub / Hugging Face / email (danielamahtoday@gmail.com)