Update README.md

README.md

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 ---
 base_model: google/gemma-2-2b-it
 library_name: peft
+tags:
+- imdb
+- sentiment-analysis
 ---
 
-# Model Card for Model ID
+# Model Card for Fine-Tuned `gemma-2-2b-it` on IMDb Sentiment Analysis
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Summary
 
+This model is a fine-tuned version of `google/gemma-2-2b-it` using **LoRA (Low-Rank Adaptation)** for efficient parameter tuning. It was trained on the IMDb dataset for binary sentiment classification (positive and negative), optimized using **4-bit quantization (NF4)** via **BitsAndBytes** for memory and computation efficiency.
 
+You can find the model and its details on Hugging Face Hub [here](https://huggingface.co/pengsu/MLB-care-for-mind-eng).
 
 ## Model Details
 
-### Model Description
+### Developed By:
+This model was fine-tuned by [Your Name or Organization] using Hugging Face's `peft` and `transformers` libraries with the IMDb dataset for English sentiment analysis.
 
-<!-- Provide a longer summary of what this model is. -->
+### Model Type:
+This is a transformer-based model for **binary sentiment classification** using the IMDb dataset.
 
+### Language:
+- **Language(s)**: English (IMDb movie reviews)
 
+### License:
+[Add relevant license here]
 
-- **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]
+### Finetuned From:
+- **Base Model**: `google/gemma-2-2b-it`
 
-### Model Sources [optional]
+### Framework Versions:
+- **Transformers**: 4.44.2
+- **PEFT**: 0.12.0
+- **Datasets**: 3.0.1
+- **PyTorch**: 2.4.1+cu121
 
-<!-- Provide the basic links for the model. -->
+## Intended Uses & Limitations
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Intended Use:
+This model can be used to classify movie reviews as **positive** or **negative**. It's well-suited for tasks like review analysis, social media sentiment classification, or feedback systems.
 
-## Uses
+### Out-of-Scope Use:
+The model may not perform well on tasks that require multi-class sentiment classification or text outside of the domain of English movie reviews.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Limitations:
+- **Bias**: Since the model is trained on IMDb data, it may reflect the dataset's biases and could be less accurate when applied to different domains or types of sentiment analysis.
+- **Generalization**: The model may not generalize well to other forms of text, such as product reviews or social media comments, without additional fine-tuning.
 
-### Direct Use
+## Model Architecture
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### Quantization:
+The model leverages **4-bit quantization** (NF4) using `BitsAndBytes` to make it more memory-efficient. This allows the model to be run on smaller hardware resources while maintaining competitive performance.
 
-[More Information Needed]
+### LoRA Configuration:
+The model uses **Low-Rank Adaptation (LoRA)** to efficiently fine-tune a subset of parameters. The specific modules adapted include:
+- `down_proj`, `gate_proj`, `q_proj`, `o_proj`, `up_proj`, `v_proj`, `k_proj`.
 
-### 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]
-
-### 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]
+The LoRA configuration is:
+- `r = 16`, `lora_alpha = 32`, `lora_dropout = 0.05`
 
 ## 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. -->
+### Dataset:
+The model was trained on the **IMDb dataset**, which contains 50,000 labeled movie reviews, split into 25,000 training examples and 25,000 test examples. Each review is labeled as either **positive** or **negative**.
 
-[More Information Needed]
+- **Train Set Size**: 25,000 samples
+- **Test Set Size**: 25,000 samples
+- **Classes**: 2 (POSITIVE, NEGATIVE)
 
-### Training Procedure
+### Preprocessing:
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Text from IMDb reviews was tokenized using the `google/gemma-2-2b-it` tokenizer with a maximum sequence length of 64. The tokenization included padding and truncation to ensure consistent input lengths.
 
-#### Preprocessing [optional]
+### Hyperparameters:
 
-[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]
+- **Learning Rate**: 2e-5
+- **Batch Size (train)**: 8
+- **Batch Size (eval)**: 8
+- **Epochs**: 5
+- **Optimizer**: AdamW (with 8-bit optimization)
+- **Weight Decay**: 0.01
+- **Gradient Accumulation Steps**: 2
+- **Evaluation Steps**: 1000
+- **Logging Steps**: 1000
+- **4-bit Quantization**: Enabled (via `BitsAndBytes`)
+- **Metric for Best Model**: Accuracy
 
 ## 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]
-
-#### 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]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Metrics:
+The model was evaluated on the IMDb test dataset using the following metrics:
+- **Accuracy**
+- **F1 Score** (weighted)
+- **Precision**
+- **Recall**
 
-[More Information Needed]
+The model performs well in classifying movie reviews as positive or negative, achieving strong results across all metrics. Exact evaluation numbers will depend on the specific test runs and should be provided upon evaluation completion.
 
-## More Information [optional]
+### Code Example:
 
-[More Information Needed]
+You can load the fine-tuned model and use it for inference on your own data using the code below:
 
-## Model Card Authors [optional]
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-[More Information Needed]
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("pengsu/MLB-care-for-mind-eng")
+tokenizer = AutoTokenizer.from_pretrained("pengsu/MLB-care-for-mind-eng")
 
-## Model Card Contact
+# Tokenize input text
+text = "This movie was absolutely amazing!"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
 
-[More Information Needed]
-### Framework versions
+# Get predictions
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class = logits.argmax(-1).item()
 
-- PEFT 0.12.0
+# Map prediction to label
+id2label = {0: "NEGATIVE", 1: "POSITIVE"}
+print(f"Predicted sentiment: {id2label[predicted_class]}")