Update README.md

README.md

@@ -1,12 +1,20 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- stanfordnlp/imdb
+metrics:
+- accuracy
+- f1
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
+# Model Card for bert-imdb-sentiment
 
 <!-- Provide a quick summary of what the model is/does. -->
-
+This is a fine-tuned `bert-base-uncased` model for **binary sentiment classification** on the IMDb movie reviews dataset.  
+The model predicts whether a given movie review is **positive** or **negative**.
 
 
 ## Model Details
@@ -15,63 +23,51 @@ tags: []
 
 <!-- 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.
+This model is a `BertForSequenceClassification` model fine-tuned using Hugging Face Transformers and the IMDb dataset (25,000 movie reviews).  
+The training was done using the `Trainer` API with the following configuration:
+- Tokenization with `BertTokenizer` (`bert-base-uncased`), max sequence length of 256.
+- Fine-tuned for 3 epochs with learning rate `2e-5` and mixed-precision (fp16).
+- Achieved **~91.54% accuracy** and **F1 score of ~91.54%** on the test split.
 
-- **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]
+- **Developed by:** *koushik reddy*
+- **Model type:** Transformer-based sequence classifier (`BertForSequenceClassification`)
+- **Language(s) (NLP):** English
+- **Finetuned from model :** `bert-base-uncased` ([Hugging Face link](https://huggingface.co/bert-base-uncased))
 
-### Model Sources [optional]
+### Model Sources 
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://huggingface.co/koushik-25/bert-imdb-sentiment](https://huggingface.co/koushik-25/bert-imdb-sentiment)
+- **Paper :** Original BERT paper: *Devlin et al., 2018* ([https://arxiv.org/abs/1810.04805](https://arxiv.org/abs/1810.04805))
+- **Demo :** You can test it directly using the Inference Widget on the model page.
+
+## Intended Uses & Limitations
 
-## Uses
+- ✅ Intended for sentiment classification of English movie reviews.
+- ⚠️ May not generalize well to other domains (e.g., tweets, product reviews) without additional fine-tuning.
+- ⚠️ May reflect biases present in the IMDb dataset and the original BERT pre-training corpus.
 
-<!-- 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. -->
+```python
+from transformers import BertForSequenceClassification, BertTokenizer
+import torch
 
-[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]
+# Load model from the Hub
+model = BertForSequenceClassification.from_pretrained("your-username/bert-imdb-sentiment")
+tokenizer = BertTokenizer.from_pretrained("your-username/bert-imdb-sentiment")
 
-### Recommendations
+# Inference
+inputs = tokenizer("The movie was fantastic!", return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+pred = torch.argmax(logits, dim=1).item()
+print(["NEGATIVE", "POSITIVE"][pred])
+```
 
-<!-- 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
 
@@ -79,26 +75,45 @@ Use the code below to get started with the model.
 
 <!-- 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]
+- **Dataset:** IMDb movie reviews (`datasets.load_dataset('imdb')`).
+- **Size:** 25,000 training, 25,000 test samples.
+- **Preprocessing:** Tokenization with `max_length=256` chosen based on review length histogram.
 
 ### 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]
+#### Preprocessing 
 
-[More Information Needed]
+- Text was lowercased automatically because `bert-base-uncased` is a lowercase model.
+- Each example was tokenized with padding to `max_length=256` and truncated if longer.
+- The dataset was split into train, validation, and test using:
+  - `train`: 0–20,000 samples from the training set
+  - `val`: 20,000–25,000 samples from the training set
+  - `test`: the official IMDb test split
 
 
 #### 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 -->
+- **Base Model:** `bert-base-uncased`
+- **Num Labels:** 2 (binary classification)
+- **Batch size:** 4 per device (with gradient accumulation of 16 steps, so effective batch size = 64)
+- **Learning Rate:** 2e-5
+- **Epochs:** 3
+- **Optimizer:** AdamW (default in Transformers)
+- **Mixed Precision:** fp16 mixed precision training enabled for faster training and reduced memory usage (`fp16=True` in `TrainingArguments`)
+- **Scheduler:** Linear learning rate scheduler with warmup (default)
+- **Seed:** 224
+
+
 
-#### Speeds, Sizes, Times [optional]
+#### Speeds, Sizes, Times 
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- **Training Time:** Approx. varies by GPU; typically around 15-20 minutes on T4 GPU
+- **Checkpoint Size:** ~420MB for `pytorch_model.bin` (BERT base size plus classification head).
+- **Total Parameters:** ~110 million.
 
-[More Information Needed]
 
 ## Evaluation
 
@@ -110,90 +125,39 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
+- **Dataset:** IMDb test split (25,000 reviews) held out from training.
+- **Preprocessing:** Same as training — lowercased, tokenized with `max_length=256`.
 
 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+- This model was evaluated on the overall IMDb test set only. No specific subgroup or domain disaggregation was done.
+- The model is expected to generalize well to similar English movie review sentiment but may not be robust to domain shifts.
 
-[More Information Needed]
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
-
-### 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]
-
-<!-- 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]
+- **Accuracy:** Measures the fraction of correctly classified reviews.
+- **F1 Score:** Weighted average F1 across classes to balance precision and recall.
 
-## Glossary [optional]
+## Evaluation Results
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+| Metric    | Score   |
+|-----------|---------|
+| Accuracy  | 91.54%  |
+| F1 Score  | 91.54%  |
 
-[More Information Needed]
+Evaluated on the IMDb test set.
 
-## More Information [optional]
+## Summary
 
-[More Information Needed]
+This is a fine-tuned BERT model (`bert-base-uncased`) for binary sentiment analysis on the IMDb movie reviews dataset.  
+It classifies a given movie review as **positive** or **negative** with an accuracy of **91.54%** and a weighted F1 score of **91.54%** on the test set.  
+The model was trained using the Hugging Face `transformers` library, with tokenization based on a maximum sequence length of 256 tokens to balance coverage and efficiency.
 
-## Model Card Authors [optional]
+The model is intended for English movie reviews but may generalize reasonably to similar sentiment analysis tasks on longer-form English text.
 
-[More Information Needed]
 
-## Model Card Contact
 
-[More Information Needed]