srivarthini
/

sentiment-analyzer

@@ -1,207 +1,151 @@
 ---
-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
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-### Framework versions
-- PEFT 0.18.0

 ---
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- sentiment-analysis
+- text-classification
+- nlp
+- transformers
+---
+# Sentiment Analyzer
+This repository contains a **sentiment analysis model** for classifying text based on sentiment polarity (e.g., positive, negative, neutral).
+The model is intended for experimentation, learning, and basic NLP sentiment classification tasks.
+---
+## Model Details
+### Model Description
+- **Task:** Sentiment Analysis / Text Classification
+- **Model type:** Transformer-based text classification model
+- **Pipeline type:** Text Classification
+- **Language:** English
+- **Framework:** Hugging Face Transformers
+> Note: Detailed architecture and training configuration were not explicitly documented at the time of upload.
+---
+### Developed By
+- **Author:** Srivarthini
+### License
+- License information has not been specified.
+  Users should verify licensing before using this model in production.
+---
+## Intended Uses
+### Direct Use
+This model can be used for:
+- Sentiment classification of short text
+- Customer review analysis
+- Feedback or survey sentiment analysis
+- Educational and demonstration purposes
+### Downstream Use
+- Can be integrated into NLP pipelines
+- Can be further fine-tuned on domain-specific datasets
+### Out-of-Scope Use
+- Medical, legal, or financial decision-making
+- Safety-critical or high-risk automated systems
+- Content moderation without human oversight
+---
+## How to Get Started
+### Example Usage
+```python
+from transformers import pipeline
+classifier = pipeline(
+    "sentiment-analysis",
+    model="srivarthini/sentiment-analyzer"
+)
+classifier("The service was excellent and very fast.")