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README.md
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- toxic-language-detection
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- sentiment-analysis
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# Model Card for Model ID
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This model is designed to classify whether a given text contains offensive language or not. It was trained on a set of words labeled as either "normal" or "offensive." The model is capable of distinguishing between these two categories with high accuracy.
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Finally, there is a dense layer with a single neuron at the output, using the sigmoid activation function to make a binary decision (0 — non-offensive, 1 — offensive word).
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The model is trained using the RMSprop optimizer with a low learning rate (0.0001), allowing it to train smoothly with minimal fluctuations. The binary cross-entropy loss function is used, which is ideal for binary classification tasks where the goal is to evaluate the probability of belonging to one of two categories.
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- **Developed by:** LaciaStudio
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- **Model type:** text-classification
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- **Language(s) (NLP):** Russian, English
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- **License:** cc-by-nc-4.0
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- toxic-language-detection
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- sentiment-analysis
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---
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# Model Card for Model ID
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This model is designed to classify whether a given text contains offensive language or not. It was trained on a set of words labeled as either "normal" or "offensive." The model is capable of distinguishing between these two categories with high accuracy.
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Finally, there is a dense layer with a single neuron at the output, using the sigmoid activation function to make a binary decision (0 — non-offensive, 1 — offensive word).
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The model is trained using the RMSprop optimizer with a low learning rate (0.0001), allowing it to train smoothly with minimal fluctuations. The binary cross-entropy loss function is used, which is ideal for binary classification tasks where the goal is to evaluate the probability of belonging to one of two categories.
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- **Developed by:** LaciaStudio
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- **Model type:** text-classification
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- **Language(s) (NLP):** Russian, English
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- **License:** cc-by-nc-4.0
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