Update README.md

README.md

@@ -1,207 +1,149 @@
 ---
-base_model: google/gemma-2b
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:google/gemma-2b
-- lora
-- transformers
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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 [optional]
-
-<!-- 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. -->
-
-### 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]
-
-<!-- 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
+language:
+- en
 
-Use the code below to get started with the model.
+pipeline_tag: sentiment-analysis
 
-[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]
-
-[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
+tags:
+- sentiment-analysis
+- text-classification
+- opinion-mining
+- emotion-detection
+- nlp
+- natural-language-processing
+- transformers
+- peft
+- lora
+- adapter
+- fine-tuning
+- gemma
+- gemma-2b
+- base_model:google/gemma-2b
+- base_model:adapter:google/gemma-2b
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+library_name: peft
 
-[More Information Needed]
+base_model: google/gemma-2b
 
-### Results
+license: apache-2.0
+
+model-index:
+- name: Sentiment Analyzer (LoRA Gemma-2B)
+  results:
+  - task:
+      type: text-classification
+      name: Sentiment Analysis
+    metrics:
+    - type: accuracy
+      value: not-reported
+---
 
-[More Information Needed]
+# Sentiment Analyzer (LoRA Fine-Tuned Gemma-2B)
 
-#### Summary
+## Model Overview
 
+**Sentiment Analyzer** is a **LoRA fine-tuned Gemma-2B transformer model** designed for **sentiment analysis and text classification** tasks.  
+It uses **PEFT (Parameter-Efficient Fine-Tuning)** to achieve high performance while keeping memory and compute requirements low.
 
+This model is ideal for:
+- Sentiment analysis
+- Opinion mining
+- Review classification
+- Emotion-aware text generation
+- Lightweight NLP applications
 
-## Model Examination [optional]
+---
 
-<!-- Relevant interpretability work for the model goes here -->
+## Tasks
 
-[More Information Needed]
+- Sentiment Analysis  
+- Text Classification  
 
-## Environmental Impact
+---
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## Model Details
 
-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).
+- **Developed by:** `mysmmurf12`
+- **Shared by:** `mysmmurf12`
+- **Model type:** Transformer-based Language Model
+- **Base model:** `google/gemma-2b`
+- **Fine-tuning method:** LoRA (Low-Rank Adaptation)
+- **Library:** PEFT + Transformers
+- **Language:** English
+- **License:** Apache 2.0 (inherits from base model)
 
-- **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 Sources
 
-### Model Architecture and Objective
+- **Hugging Face Repository:**  
+  https://huggingface.co/mysmmurf12/sentiment-analyzer
 
-[More Information Needed]
+- **Base Model:**  
+  https://huggingface.co/google/gemma-2b
 
-### Compute Infrastructure
+---
 
-[More Information Needed]
+## Intended Uses
 
-#### Hardware
+### ✅ Direct Use
 
-[More Information Needed]
+- Analyze sentiment of text (positive / negative / neutral)
+- Customer feedback and review analysis
+- Social media sentiment monitoring
+- Sentiment-aware chatbots
 
-#### Software
+### 🔁 Downstream Use
 
-[More Information Needed]
+- Integration into RAG pipelines
+- Further fine-tuning on domain-specific sentiment datasets
+- Deployment in APIs, Streamlit apps, or dashboards
 
-## Citation [optional]
+### 🚫 Out-of-Scope Use
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+- Medical, legal, or financial decision-making
+- Fully automated content moderation
+- Multilingual sentiment analysis (English-focused)
 
-**BibTeX:**
+---
 
-[More Information Needed]
+## Bias, Risks, and Limitations
 
-**APA:**
+- Model behavior reflects biases present in training data
+- Sentiment predictions may be less reliable on:
+  - Highly technical content
+  - Sarcasm or ambiguous statements
+- Not benchmarked against standardized sentiment datasets
 
-[More Information Needed]
+**Recommendation:**  
+Use human review for high-impact applications.
 
-## Glossary [optional]
+---
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## How to Use the Model
 
-[More Information Needed]
+### Load with Transformers + PEFT
 
-## More Information [optional]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 
-[More Information Needed]
+base_model = "google/gemma-2b"
+adapter_model = "mysmmurf12/sentiment-analyzer"
 
-## Model Card Authors [optional]
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(base_model)
 
-[More Information Needed]
+model = PeftModel.from_pretrained(model, adapter_model)
 
-## Model Card Contact
+text = "The product quality is amazing!"
+inputs = tokenizer(text, return_tensors="pt")
 
-[More Information Needed]
-### Framework versions
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=50,
+    do_sample=True,
+    temperature=0.7
+)
 
-- PEFT 0.18.0
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))