Model Card for DistilBertForSequenceClassification_6h_768dim

Model Description

Purpose: This model is designed for sentiment analysis tasks, aimed at classifying text into positive or negative sentiment categories.
Model architecture: The model is based on the DistilBERT architecture, which is a distilled version of BERT that maintains most of the original model's performance while being more efficient. Specifically, it uses 6 attention heads, a hidden dimension size of 768, and an intermediate (hidden) layer size of 4*768.
Training data: The model was fine-tuned on a dataset compiled from various sources, including social media posts, product reviews, and movie reviews. The data was preprocessed to remove usernames, URLs, and any identifiable information. Texts were lowercased, and stopwords were removed to focus on meaningful content.

Intended users: This model is intended for developers and data scientists looking to integrate sentiment analysis into their applications, such as customer feedback analysis or content moderation.
Use cases: Potential use cases include analyzing customer reviews to gauge overall sentiment about products or services, monitoring social media for brand sentiment, and filtering content based on sentiment for moderation purposes.

Known limitations: The model may exhibit biases present in the training data, potentially leading to inaccuracies in certain contexts or for specific demographic groups. Its performance has not been extensively tested across all possible domains, so results may vary for texts outside of the training distribution.

Training Platform: The model was trained on Intel Developer Cloud over scalable Intel® Xeon® 4th Gen Scalable processors.

Known Optimizations: During training, techniques such as gradient accumulation and mixed-precision training were employed to enhance performance and reduce memory usage. The AdamW optimizer was used for its effective learning rate adjustments.

Ethical concerns: There is a risk of the model reinforcing or amplifying biases present in the training data, leading to potentially unfair outcomes. Users are encouraged to thoroughly test the model in their specific contexts and consider bias mitigation strategies.

For more details on the DistilBERT model architecture and its implementation, please refer to the original paper and documentation available on the Hugging Face model hub.