Michellejieli
Models by this creator
📈
NSFW_text_classifier
75
The NSFW_text_classifier is an AI model designed to classify text as safe for work (SFW) or not safe for work (NSFW). This model can be useful for content filtering and moderation, ensuring that inappropriate or explicit content is not displayed to users. When compared to similar models like NSFW_13B_sft and LLaMA-7B, the NSFW_text_classifier offers a focused approach to text classification, rather than a more general language model. Model inputs and outputs The NSFW_text_classifier takes in text as input and outputs a classification of whether the text is safe for work (SFW) or not safe for work (NSFW). This can be a useful tool for content moderation, ensuring that inappropriate or explicit content is not displayed to users. Inputs Text**: The text to be classified as SFW or NSFW. Outputs SFW/NSFW Classification**: The model's prediction of whether the input text is safe for work (SFW) or not safe for work (NSFW). Capabilities The NSFW_text_classifier is capable of accurately identifying text that may be inappropriate or explicit, which can be useful for content filtering and moderation. By using this model, you can ensure that your platform or application only displays content that is suitable for a wide audience. What can I use it for? The NSFW_text_classifier can be used in a variety of applications that require content moderation, such as social media platforms, messaging apps, and online forums. By integrating this model into your application, you can automatically flag and filter out NSFW content, ensuring a safer and more enjoyable experience for your users. Additionally, the NSFW_text_classifier can be useful for maintaining a positive brand image and building trust with your audience. Things to try One interesting thing to try with the NSFW_text_classifier is to experiment with different types of text input, such as slang, profanity, or innuendo, to see how the model responds. By understanding the model's strengths and limitations, you can better integrate it into your application and ensure that it is effectively filtering out inappropriate content.
Updated 5/28/2024
🛸
emotion_text_classifier
51
The emotion_text_classifier model is a fine-tuned version of the DistilRoBERTa-base model for emotion classification. It was developed by maintainer michellejieli and trained on transcripts from the Friends TV show. The model can predict 6 Ekman emotions (anger, disgust, fear, joy, sadness, surprise) as well as a neutral class from text data, such as dialogue from movies or TV shows. The emotion_text_classifier model is similar to other fine-tuned BERT-based models for emotion recognition, such as the distilbert-base-uncased-emotion model. These models leverage the power of large language models like BERT and DistilRoBERTa to achieve strong performance on the emotion classification task. Model inputs and outputs Inputs Text**: The model takes in a single text input, which can be a sentence, paragraph, or longer text excerpt. Outputs Emotion labels**: The model outputs a list of emotion labels and their corresponding probability scores. The possible emotion labels are anger, disgust, fear, joy, neutrality, sadness, and surprise. Capabilities The emotion_text_classifier model can accurately predict the emotional state expressed in a given text, which can be useful for applications like sentiment analysis, content moderation, and customer service chatbots. For example, the model can identify that the text "I love this!" expresses joy with a high probability. What can I use it for? The emotion_text_classifier model can be used in a variety of applications that require understanding the emotional tone of text data. Some potential use cases include: Sentiment analysis**: Analyzing customer reviews or social media posts to gauge public sentiment towards a product or brand. Affective computing**: Developing intelligent systems that can recognize and respond to human emotions, such as chatbots or digital assistants. Content moderation**: Flagging potentially harmful or inappropriate content based on the emotional tone. Behavioral analysis**: Understanding the emotional state of individuals in areas like mental health, education, or human resources. Things to try One interesting aspect of the emotion_text_classifier model is its ability to distinguish between nuanced emotional states, such as the difference between anger and disgust. Experimenting with a variety of input texts, from everyday conversations to more complex emotional expressions, can provide insights into the model's capabilities and limitations. Additionally, you could explore using the model in combination with other NLP techniques, such as topic modeling or named entity recognition, to gain a more holistic understanding of the emotional content in a given text corpus.
Updated 5/28/2024