t5-base-finetuned-span-sentiment-extraction
| Property | Value |
|---|---|
| Author | mrm8488 |
| Downloads | 29,563 |
| Research Paper | Link to Paper |
| Training Data | Tweet Sentiment Extraction Dataset (27,480 samples) |
What is t5-base-finetuned-span-sentiment-extraction?
This model is a fine-tuned version of Google's T5-base architecture specifically trained for sentiment span extraction from text. It excels at identifying the specific words or phrases within a text that contribute to its overall sentiment, making it particularly valuable for detailed sentiment analysis tasks.
Implementation Details
The model builds upon the T5 architecture and has been fine-tuned on the Tweet Sentiment Extraction Dataset from Kaggle. It processes input in a text-to-text format, where queries are structured as "question: [sentiment] context: [text]" and outputs the relevant span of text that expresses the specified sentiment.
- Built on T5-base architecture
- Fine-tuned on 23,907 training samples
- Evaluated on 3,573 test samples
- Supports positive, negative, and neutral sentiment extraction
Core Capabilities
- Precise extraction of sentiment-bearing phrases from text
- Handles complex tweet-style content
- Supports multi-word span identification
- Real-time sentiment analysis with specific text highlighting
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ability to not just classify sentiment but to precisely identify the specific text spans that contribute to that sentiment, making it invaluable for detailed sentiment analysis and content moderation.
Q: What are the recommended use cases?
The model is particularly well-suited for social media monitoring, brand sentiment analysis, customer feedback analysis, and any application requiring granular understanding of sentiment expressions in text.
