riturajpandey739
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gpt2-sentiment-analysis-tweets

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----
-library_name: transformers
-license: mit
-datasets:
-- mteb/tweet_sentiment_extraction
-language:
-- en
-metrics:
-- accuracy
-base_model:
-- openai-community/gpt2
-pipeline_tag: text-classification
----
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+# GPT-2 Sentiment Analysis for Tweets
 ## Model Details
+- **Model Type**: GPT-2 (Fine-tuned for sentiment analysis)
+- **Model Architecture**: Transformer-based language model (GPT-2)
+- **Fine-tuned On**: `mteb/tweet_sentiment_extraction` dataset
+- **Intended Task**: Sentiment Classification (Tweet Sentiment)
+## Model Overview
+This model is a fine-tuned version of GPT-2, trained to classify tweets into sentiment categories. The model was fine-tuned on the **mteb/tweet_sentiment_extraction** dataset, which contains labeled tweets for sentiment analysis.
+The model performs the task of classifying tweets into three sentiment categories:
+- **Negative**: Label 0
+- **Neutral**: Label 1
+- **Positive**: Label 2
+This model is suitable for analyzing sentiment in short-form text such as tweets, product reviews, or customer feedback.
+## Intended Use
+The model can be used for the following purposes:
+- **Sentiment analysis** of short texts (e.g., tweets, reviews, feedback).
+- **Customer feedback analysis** to classify sentiment in user comments.
+- **Social media monitoring** to track the sentiment of public opinion about topics, brands, or products.
+## How to Use
+You can use the model with the Hugging Face `pipeline` API to classify the sentiment of a text input.
+#### Example:
+```python
+from transformers import pipeline
+# Load the fine-tuned model
+classifier = pipeline("text-classification", model="your-username/gpt2-sentiment-analysis-tweets")
+# Example text for sentiment classification
+text = "This product is amazing! I absolutely love it."
+# Get the sentiment prediction
+result = classifier(text)
+# Output the result
+print(result)
+# Example Output: [{'label': 'Positive', 'score': 0.999}]