Update README.md

README.md

@@ -1,199 +1,157 @@
 ---
 library_name: transformers
-tags: []
+tags:
+  - text-classification
+  - sentiment-analysis
+  - imdb
+  - bert
+  - colab
+  - huggingface
+  - fine-tuned
+license: apache-2.0
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# 🤖 BERT IMDb Sentiment Classifier
 
+A fine-tuned `bert-base-uncased` model for **binary sentiment classification** on the [IMDb movie reviews dataset](https://huggingface.co/datasets/imdb).  
+Trained in Google Colab using Hugging Face Transformers with ~93% test accuracy.
 
+---
 
-## Model Details
+## 📌 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:** Shubham Swarnakar
+- **Shared by:** [ShubhamSwarnakar](https://huggingface.co/ShubhamSwarnakar)
+- **Model type:** `BERTForSequenceClassification`
+- **Language(s):** English 🇺🇸
+- **License:** Apache-2.0
+- **Fine-tuned from:** [bert-base-uncased](https://huggingface.co/bert-base-uncased)
 
-- **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
 
-### Model Sources [optional]
+- **Repository:** https://huggingface.co/ShubhamSwarnakar/bert-imdb-colab-model
+- **Demo:** Available via Hugging Face Inference Widget
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
+## ✅ Uses
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Use this model for **sentiment analysis** on English movie reviews or similar texts.  
+Returns either a `positive` or `negative` classification.
 
-[More Information Needed]
+### Downstream Use
 
-### 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]
+Can be fine-tuned further for domain-specific sentiment classification tasks.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+Not designed for:
+- Multilingual sentiment analysis
+- Nuanced emotion detection (e.g., joy, anger, sarcasm)
+- Non-movie domains without re-training
 
-## Bias, Risks, and Limitations
+---
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## ⚠️ Bias, Risks, and Limitations
 
-[More Information Needed]
+This model inherits potential biases from:
+- Pretrained BERT weights
+- IMDb dataset (may reflect demographic or cultural skew)
 
 ### 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]
-
-## 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]
+Avoid deploying this model in high-risk applications without auditing or further fine-tuning. Misclassification risk exists, especially with ambiguous or sarcastic text.
 
-[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]
-
-## 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]
+## 🚀 How to Get Started
 
-#### Summary
+```python
+from transformers import pipeline
 
+classifier = pipeline("sentiment-analysis", model="ShubhamSwarnakar/bert-imdb-colab-model")
+classifier("This movie was surprisingly entertaining!")
 
 
-## Model Examination [optional]
 
-<!-- Relevant interpretability work for the model goes here -->
 
-[More Information Needed]
+🧠 Training Details
+Training Data
+Dataset: IMDb Dataset
 
-## Environmental Impact
+Format: Binary sentiment (positive = 1, negative = 0)
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Training Procedure
+Preprocessing: Tokenized with BertTokenizerFast
 
-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).
+Epochs: 3
 
-- **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]
+Optimizer: AdamW
 
-## Technical Specifications [optional]
+Scheduler: Linear LR
 
-### Model Architecture and Objective
+Batch size: 8
 
-[More Information Needed]
+Trained using Colab with limited GPU resources
 
-### Compute Infrastructure
+📊 Evaluation
+Metrics
 
-[More Information Needed]
+Final test accuracy: 93.47%
 
-#### Hardware
+Results Summary
+Epoch	Validation Accuracy
+1	        91.80%
+2	        92.04%
+3	        92.92%
 
-[More Information Needed]
+Final test accuracy on held-out IMDb test split: 93.47%
 
-#### Software
+🌱 Environmental Impact
+Estimated based on lightweight training:
 
-[More Information Needed]
+Hardware Type: Google Colab GPU (T4)
 
-## Citation [optional]
+Training Duration: ~2 hours
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Cloud Provider: Google
 
-**BibTeX:**
+Region: Unknown
 
-[More Information Needed]
+Emissions Estimate: ~0.15 kg CO₂eq
 
-**APA:**
+Estimate via ML CO2 Impact Calculator
 
-[More Information Needed]
+🏗️ Technical Specifications
+Architecture
+BERT-base (12-layer, 768-hidden, 12-heads, 110M parameters)
 
-## Glossary [optional]
+Compute Infrastructure
+Hardware: Google Colab with GPU
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Software:
 
-[More Information Needed]
+Python 3.11
 
-## More Information [optional]
+Transformers 4.x
 
-[More Information Needed]
+Datasets
 
-## Model Card Authors [optional]
+PyTorch 2.x
 
-[More Information Needed]
+📚 Citation
 
-## Model Card Contact
+@misc{shubhamswarnakar_bert_imdb_2025,
+  author       = {Shubham Swarnakar},
+  title        = {BERT IMDb Sentiment Classifier},
+  year         = 2025,
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/ShubhamSwarnakar/bert-imdb-colab-model}},
+}
 
-[More Information Needed]
+🙋 More Info
+For questions or collaboration, contact @ShubhamSwarnakar.