rbelanec/train_sst2_42_1773765558
The rbelanec/train_sst2_42_1773765558 model is a 1 billion parameter language model fine-tuned from meta-llama/Llama-3.2-1B-Instruct. It is specifically optimized for sentiment analysis tasks, having been trained on the SST-2 dataset. This model achieves a validation loss of 0.1337, indicating its proficiency in classifying the sentiment of text inputs. Its primary use case is sentiment classification, leveraging its fine-tuned capabilities for accurate sentiment prediction.
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Model Overview
This model, rbelanec/train_sst2_42_1773765558, is a fine-tuned version of the meta-llama/Llama-3.2-1B-Instruct base model, featuring 1 billion parameters. It has been specifically adapted for sentiment analysis by training on the SST-2 dataset.
Key Capabilities
- Sentiment Classification: Optimized for determining the sentiment of text inputs.
- Performance: Achieved a validation loss of 0.1337 on the evaluation set, demonstrating its effectiveness in sentiment analysis.
- Training Details: Trained with a learning rate of 5e-05 over 5 epochs, utilizing an AdamW optimizer and a cosine learning rate scheduler.
Intended Use Cases
This model is particularly well-suited for applications requiring:
- Automated Sentiment Analysis: Classifying customer reviews, social media posts, or feedback.
- Text Understanding: Gaining insights into the emotional tone of written content.
Limitations
As a specialized model, its performance outside of sentiment analysis tasks may be limited. Further details on specific limitations and broader intended uses are not provided in the original model card.