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tiny-guard-4m-en-prompt-harmfulness-binary-moderation

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  ---
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- base_model: unknown
 
 
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  library_name: model2vec
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  license: mit
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- model_name: tmp1m0sv0n_
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  tags:
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- - embeddings
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  - static-embeddings
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- - sentence-transformers
 
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  ---
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- # tmp1m0sv0n_ Model Card
 
 
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- This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the unknown(https://huggingface.co/unknown) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
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  ## Installation
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- Install model2vec using pip:
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- ```
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- pip install model2vec
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  ```
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  ## Usage
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- ### Using Model2Vec
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-
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- The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
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-
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- Load this model using the `from_pretrained` method:
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  ```python
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- from model2vec import StaticModel
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-
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- # Load a pretrained Model2Vec model
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- model = StaticModel.from_pretrained("tmp1m0sv0n_")
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-
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- # Compute text embeddings
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- embeddings = model.encode(["Example sentence"])
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- ```
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- ### Using Sentence Transformers
 
 
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- You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
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- ```python
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- from sentence_transformers import SentenceTransformer
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- # Load a pretrained Sentence Transformer model
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- model = SentenceTransformer("tmp1m0sv0n_")
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- # Compute text embeddings
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- embeddings = model.encode(["Example sentence"])
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  ```
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- ### Distilling a Model2Vec model
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-
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- You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
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-
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- ```python
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- from model2vec.distill import distill
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-
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- # Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
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- m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
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-
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- # Save the model
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- m2v_model.save_pretrained("m2v_model")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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-
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- ## How it works
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-
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- Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
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-
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- It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
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-
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- ## Additional Resources
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-
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- - [Model2Vec Repo](https://github.com/MinishLab/model2vec)
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- - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
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- - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
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- - [Model2Vec Docs](https://minish.ai/packages/model2vec/introduction)
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-
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-
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- ## Library Authors
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-
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- Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Citation
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- Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
 
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  ```
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  @software{minishlab2024model2vec,
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  author = {Stephan Tulkens and {van Dongen}, Thomas},
 
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  ---
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+ base_model: minishlab/potion-base-4m
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+ datasets:
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+ - enguard/multi-lingual-prompt-moderation
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  library_name: model2vec
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  license: mit
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+ model_name: enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation
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  tags:
 
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  - static-embeddings
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+ - text-classification
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+ - model2vec
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  ---
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+ # enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation
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+
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+ This model is a fine-tuned Model2Vec classifier based on [minishlab/potion-base-4m](https://huggingface.co/minishlab/potion-base-4m) for the prompt-harmfulness-binary found in the [enguard/multi-lingual-prompt-moderation](https://huggingface.co/datasets/enguard/multi-lingual-prompt-moderation) dataset.
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  ## Installation
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+ ```bash
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+ pip install model2vec[inference]
 
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  ```
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  ## Usage
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  ```python
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+ from model2vec.inference import StaticModelPipeline
 
 
 
 
 
 
 
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+ model = StaticModelPipeline.from_pretrained(
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+ "enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation"
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+ )
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+ # Supports single texts. Format input as a single text:
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+ text = "Example sentence"
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+ model.predict([text])
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+ model.predict_proba([text])
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  ```
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+ ## Why should you use these models?
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+
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+ - Optimized for precision to reduce false positives.
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+ - Extremely fast inference: up to x500 faster than SetFit.
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+
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+ ## This model variant
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+
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+ Below is a quick overview of the model variant and core metrics.
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+
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+ | Field | Value |
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+ |---|---|
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+ | Classifies | prompt-harmfulness-binary |
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+ | Base Model | [minishlab/potion-base-4m](https://huggingface.co/minishlab/potion-base-4m) |
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+ | Precision | 0.8705 |
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+ | Precision | 0.8705 |
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+ | Recall | 0.7157 |
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+ | F1 | 0.7855 |
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+
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+ ### Confusion Matrix
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+
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+ | | FAIL | PASS |
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+ | --- | --- | --- |
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+ | **FAIL** | 1948 | 778 |
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+ | **PASS** | 288 | 2438 |
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+
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+ <details>
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+ <summary><b>Full metrics (JSON)</b></summary>
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+
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+ ```json
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+ {
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+ "FAIL": {
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+ "precision": 0.8705197132616488,
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+ "recall": 0.7156537753222836,
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+ "f1-score": 0.7855265817667273,
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+ "support": 2715.0
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+ },
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+ "PASS": {
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+ "precision": 0.7573852922690132,
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+ "recall": 0.8929233049277511,
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+ "f1-score": 0.819588505356232,
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+ "support": 2699.0
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+ },
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+ "accuracy": 0.8040265977096417,
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+ "macro avg": {
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+ "precision": 0.8139525027653309,
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+ "recall": 0.8042885401250173,
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+ "f1-score": 0.8025575435614796,
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+ "support": 5414.0
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+ },
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+ "weighted avg": {
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+ "precision": 0.814119675903111,
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+ "recall": 0.8040265977096417,
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+ "f1-score": 0.8025072119418424,
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+ "support": 5414.0
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+ }
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+ }
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  ```
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+ </details>
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+
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+
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+ <details>
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+ <summary><b>Sample Predictions</b></summary>
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+
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+ | Text | True Label | Predicted Label |
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+ |------|------------|-----------------|
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+ | If you can fold your penis into the shape of a vulva, you deserve not to be caught. In fact you deserve an ace origami prize. | FAIL | FAIL |
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+ | I'm surprised they let you stay without buying anything. | PASS | PASS |
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+ | Pirates flew the [Jolly Roger](http://i.imgur.com/o9pyLSe.jpg).\n\nPimps blew the [roger](http://en.wikipedia.org/wiki/Roger#Slang) jolly. | PASS | PASS |
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+ | Seriously?<br><br>You're "depressed" because of a story? Oh, Reddit. | PASS | PASS |
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+ | Yeah what kind of community manager publicly mocks their players achievements? With no clarification, straight douche. | FAIL | FAIL |
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+ | If you can fold your penis into the shape of a vulva, you deserve not to be caught. In fact you deserve an ace origami prize. | FAIL | FAIL |
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+ </details>
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+
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+
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+ <details>
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+ <summary><b>Prediction Speed Benchmarks</b></summary>
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+
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+ | Dataset Size | Time (seconds) | Predictions/Second |
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+ |--------------|----------------|---------------------|
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+ | 1 | 0.0002 | 5178.15 |
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+ | 1000 | 0.0589 | 16990.69 |
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+ | 5452 | 0.2874 | 18967.45 |
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+ </details>
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+
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+
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+ ## Other model variants
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+
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+ Below is a general overview of the best-performing models for each dataset variant.
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+
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+ | Classifies | Model | Precision | Recall | F1 |
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+ | --- | --- | --- | --- | --- |
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+ | prompt-harassment-binary | [enguard/tiny-guard-2m-en-prompt-harassment-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-harassment-binary-moderation) | 0.8597 | 0.7620 | 0.8079 |
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+ | prompt-harmfulness-binary | [enguard/tiny-guard-2m-en-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-harmfulness-binary-moderation) | 0.8826 | 0.6479 | 0.7472 |
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+ | prompt-hate-speech-alt-binary | [enguard/tiny-guard-2m-en-prompt-hate-speech-alt-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-hate-speech-alt-binary-moderation) | 1.0000 | 0.6364 | 0.7778 |
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+ | prompt-hate-speech-binary | [enguard/tiny-guard-2m-en-prompt-hate-speech-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-hate-speech-binary-moderation) | 0.8436 | 0.8233 | 0.8333 |
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+ | prompt-hate-speech-combined-binary | [enguard/tiny-guard-2m-en-prompt-hate-speech-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-hate-speech-combined-binary-moderation) | 0.9141 | 0.7269 | 0.8098 |
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+ | prompt-self-harm-binary | [enguard/tiny-guard-2m-en-prompt-self-harm-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-self-harm-binary-moderation) | 0.8929 | 0.7143 | 0.7937 |
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+ | prompt-sexual-content-binary | [enguard/tiny-guard-2m-en-prompt-sexual-content-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-sexual-content-binary-moderation) | 0.9042 | 0.8510 | 0.8768 |
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+ | prompt-sexual-content-combined-binary | [enguard/tiny-guard-2m-en-prompt-sexual-content-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-sexual-content-combined-binary-moderation) | 0.9264 | 0.8021 | 0.8598 |
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+ | prompt-sexual-content-minors-binary | [enguard/tiny-guard-2m-en-prompt-sexual-content-minors-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-sexual-content-minors-binary-moderation) | 0.9123 | 0.8387 | 0.8739 |
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+ | prompt-violence-alt-binary | [enguard/tiny-guard-2m-en-prompt-violence-alt-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-violence-alt-binary-moderation) | 0.8621 | 0.8065 | 0.8333 |
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+ | prompt-violence-binary | [enguard/tiny-guard-2m-en-prompt-violence-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-violence-binary-moderation) | 0.8953 | 0.8370 | 0.8652 |
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+ | prompt-violence-combined-binary | [enguard/tiny-guard-2m-en-prompt-violence-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-violence-combined-binary-moderation) | 0.9056 | 0.7645 | 0.8291 |
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+ | prompt-harassment-binary | [enguard/tiny-guard-4m-en-prompt-harassment-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-harassment-binary-moderation) | 0.8885 | 0.7165 | 0.7933 |
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+ | prompt-harmfulness-binary | [enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation) | 0.8705 | 0.7157 | 0.7855 |
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+ | prompt-hate-speech-alt-binary | [enguard/tiny-guard-4m-en-prompt-hate-speech-alt-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-hate-speech-alt-binary-moderation) | 1.0000 | 0.6364 | 0.7778 |
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+ | prompt-hate-speech-binary | [enguard/tiny-guard-4m-en-prompt-hate-speech-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-hate-speech-binary-moderation) | 0.8589 | 0.8313 | 0.8449 |
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+ | prompt-hate-speech-combined-binary | [enguard/tiny-guard-4m-en-prompt-hate-speech-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-hate-speech-combined-binary-moderation) | 0.9198 | 0.7831 | 0.8460 |
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+ | prompt-self-harm-binary | [enguard/tiny-guard-4m-en-prompt-self-harm-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-self-harm-binary-moderation) | 0.9062 | 0.8286 | 0.8657 |
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+ | prompt-sexual-content-binary | [enguard/tiny-guard-4m-en-prompt-sexual-content-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-sexual-content-binary-moderation) | 0.9316 | 0.8735 | 0.9016 |
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+ | prompt-sexual-content-combined-binary | [enguard/tiny-guard-4m-en-prompt-sexual-content-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-sexual-content-combined-binary-moderation) | 0.9356 | 0.8503 | 0.8909 |
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+ | prompt-sexual-content-minors-binary | [enguard/tiny-guard-4m-en-prompt-sexual-content-minors-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-sexual-content-minors-binary-moderation) | 0.9138 | 0.8548 | 0.8833 |
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+ | prompt-violence-alt-binary | [enguard/tiny-guard-4m-en-prompt-violence-alt-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-violence-alt-binary-moderation) | 0.9231 | 0.7742 | 0.8421 |
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+ | prompt-violence-binary | [enguard/tiny-guard-4m-en-prompt-violence-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-violence-binary-moderation) | 0.8902 | 0.8225 | 0.8550 |
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+ | prompt-violence-combined-binary | [enguard/tiny-guard-4m-en-prompt-violence-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-violence-combined-binary-moderation) | 0.8851 | 0.8370 | 0.8603 |
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+ | prompt-harassment-binary | [enguard/tiny-guard-8m-en-prompt-harassment-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-harassment-binary-moderation) | 0.8787 | 0.8166 | 0.8465 |
160
+ | prompt-harmfulness-binary | [enguard/tiny-guard-8m-en-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-harmfulness-binary-moderation) | 0.8641 | 0.7632 | 0.8105 |
161
+ | prompt-hate-speech-alt-binary | [enguard/tiny-guard-8m-en-prompt-hate-speech-alt-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-hate-speech-alt-binary-moderation) | 1.0000 | 0.7273 | 0.8421 |
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+ | prompt-hate-speech-binary | [enguard/tiny-guard-8m-en-prompt-hate-speech-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-hate-speech-binary-moderation) | 0.8612 | 0.8474 | 0.8543 |
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+ | prompt-hate-speech-combined-binary | [enguard/tiny-guard-8m-en-prompt-hate-speech-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-hate-speech-combined-binary-moderation) | 0.9152 | 0.8233 | 0.8668 |
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+ | prompt-self-harm-binary | [enguard/tiny-guard-8m-en-prompt-self-harm-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-self-harm-binary-moderation) | 0.9667 | 0.8286 | 0.8923 |
165
+ | prompt-sexual-content-binary | [enguard/tiny-guard-8m-en-prompt-sexual-content-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-sexual-content-binary-moderation) | 0.9228 | 0.9116 | 0.9172 |
166
+ | prompt-sexual-content-combined-binary | [enguard/tiny-guard-8m-en-prompt-sexual-content-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-sexual-content-combined-binary-moderation) | 0.9411 | 0.8795 | 0.9093 |
167
+ | prompt-sexual-content-minors-binary | [enguard/tiny-guard-8m-en-prompt-sexual-content-minors-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-sexual-content-minors-binary-moderation) | 0.8871 | 0.8871 | 0.8871 |
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+ | prompt-violence-alt-binary | [enguard/tiny-guard-8m-en-prompt-violence-alt-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-violence-alt-binary-moderation) | 0.9032 | 0.9032 | 0.9032 |
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+ | prompt-violence-binary | [enguard/tiny-guard-8m-en-prompt-violence-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-violence-binary-moderation) | 0.8826 | 0.8442 | 0.8630 |
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+ | prompt-violence-combined-binary | [enguard/tiny-guard-8m-en-prompt-violence-combined-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-violence-combined-binary-moderation) | 0.9046 | 0.8587 | 0.8810 |
171
+ | prompt-harassment-binary | [enguard/small-guard-32m-en-prompt-harassment-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-harassment-binary-moderation) | 0.8680 | 0.8307 | 0.8490 |
172
+ | prompt-harmfulness-binary | [enguard/small-guard-32m-en-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-harmfulness-binary-moderation) | 0.8717 | 0.7808 | 0.8238 |
173
+ | prompt-hate-speech-alt-binary | [enguard/small-guard-32m-en-prompt-hate-speech-alt-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-hate-speech-alt-binary-moderation) | 1.0000 | 0.8182 | 0.9000 |
174
+ | prompt-hate-speech-binary | [enguard/small-guard-32m-en-prompt-hate-speech-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-hate-speech-binary-moderation) | 0.8601 | 0.8394 | 0.8496 |
175
+ | prompt-hate-speech-combined-binary | [enguard/small-guard-32m-en-prompt-hate-speech-combined-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-hate-speech-combined-binary-moderation) | 0.9200 | 0.8313 | 0.8734 |
176
+ | prompt-self-harm-binary | [enguard/small-guard-32m-en-prompt-self-harm-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-self-harm-binary-moderation) | 0.9333 | 0.8000 | 0.8615 |
177
+ | prompt-sexual-content-binary | [enguard/small-guard-32m-en-prompt-sexual-content-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-sexual-content-binary-moderation) | 0.9205 | 0.9029 | 0.9116 |
178
+ | prompt-sexual-content-combined-binary | [enguard/small-guard-32m-en-prompt-sexual-content-combined-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-sexual-content-combined-binary-moderation) | 0.9327 | 0.8830 | 0.9072 |
179
+ | prompt-sexual-content-minors-binary | [enguard/small-guard-32m-en-prompt-sexual-content-minors-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-sexual-content-minors-binary-moderation) | 0.8852 | 0.8710 | 0.8780 |
180
+ | prompt-violence-alt-binary | [enguard/small-guard-32m-en-prompt-violence-alt-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-violence-alt-binary-moderation) | 0.9333 | 0.9032 | 0.9180 |
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+ | prompt-violence-binary | [enguard/small-guard-32m-en-prompt-violence-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-violence-binary-moderation) | 0.8714 | 0.8841 | 0.8777 |
182
+ | prompt-violence-combined-binary | [enguard/small-guard-32m-en-prompt-violence-combined-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-violence-combined-binary-moderation) | 0.9077 | 0.8913 | 0.8995 |
183
+ | prompt-harassment-binary | [enguard/medium-guard-128m-xx-prompt-harassment-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-harassment-binary-moderation) | 0.8650 | 0.7994 | 0.8309 |
184
+ | prompt-hate-speech-alt-binary | [enguard/medium-guard-128m-xx-prompt-hate-speech-alt-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-hate-speech-alt-binary-moderation) | 0.8889 | 0.7273 | 0.8000 |
185
+ | prompt-hate-speech-binary | [enguard/medium-guard-128m-xx-prompt-hate-speech-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-hate-speech-binary-moderation) | 0.8367 | 0.8434 | 0.8400 |
186
+ | prompt-hate-speech-combined-binary | [enguard/medium-guard-128m-xx-prompt-hate-speech-combined-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-hate-speech-combined-binary-moderation) | 0.8826 | 0.8153 | 0.8476 |
187
+ | prompt-self-harm-binary | [enguard/medium-guard-128m-xx-prompt-self-harm-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-self-harm-binary-moderation) | 0.9375 | 0.8571 | 0.8955 |
188
+ | prompt-sexual-content-binary | [enguard/medium-guard-128m-xx-prompt-sexual-content-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-sexual-content-binary-moderation) | 0.9050 | 0.8752 | 0.8899 |
189
+ | prompt-sexual-content-combined-binary | [enguard/medium-guard-128m-xx-prompt-sexual-content-combined-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-sexual-content-combined-binary-moderation) | 0.9152 | 0.8726 | 0.8934 |
190
+ | prompt-sexual-content-minors-binary | [enguard/medium-guard-128m-xx-prompt-sexual-content-minors-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-sexual-content-minors-binary-moderation) | 0.9310 | 0.8710 | 0.9000 |
191
+ | prompt-violence-alt-binary | [enguard/medium-guard-128m-xx-prompt-violence-alt-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-violence-alt-binary-moderation) | 0.9000 | 0.8710 | 0.8852 |
192
+ | prompt-violence-binary | [enguard/medium-guard-128m-xx-prompt-violence-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-violence-binary-moderation) | 0.9057 | 0.8696 | 0.8872 |
193
+ | prompt-violence-combined-binary | [enguard/medium-guard-128m-xx-prompt-violence-combined-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-violence-combined-binary-moderation) | 0.8821 | 0.8406 | 0.8609 |
194
+
195
+ ## Resources
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+
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+ - Awesome AI Guardrails: <https://github.com/enguard-ai/awesome-ai-guardails>
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+ - Model2Vec: https://github.com/MinishLab/model2vec
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+ - Docs: https://minish.ai/packages/model2vec/introduction
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  ## Citation
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+ If you use this model, please cite Model2Vec:
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+
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  ```
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  @software{minishlab2024model2vec,
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  author = {Stephan Tulkens and {van Dongen}, Thomas},