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tiny-guard-4m-en-general-safety-hr-binary-guardset

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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: tmp7pj7pi8a
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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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- # tmp7pj7pi8a 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("tmp7pj7pi8a")
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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("tmp7pj7pi8a")
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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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+ - AI-Secure/PolyGuard
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  library_name: model2vec
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  license: mit
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+ model_name: enguard/tiny-guard-4m-en-general-safety-hr-binary-guardset
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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-general-safety-hr-binary-guardset
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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 general-safety-hr-binary found in the [AI-Secure/PolyGuard](https://huggingface.co/datasets/AI-Secure/PolyGuard) 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-general-safety-hr-binary-guardset"
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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 | general-safety-hr-binary |
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+ | Base Model | [minishlab/potion-base-4m](https://huggingface.co/minishlab/potion-base-4m) |
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+ | Precision | 0.9724 |
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+ | Recall | 0.9267 |
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+ | F1 | 0.9490 |
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+
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+ ### Confusion Matrix
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+
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+ | True \ Predicted | FAIL | PASS |
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+ | --- | --- | --- |
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+ | **FAIL** | 668 | 55 |
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+ | **PASS** | 19 | 633 |
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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": 0.9724238026124818,
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+ "recall": 0.9266943291839558,
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+ "f1-score": 0.9490084985835694,
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+ "support": 723.0
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+ },
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+ "PASS": {
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+ "precision": 0.922740524781341,
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+ "recall": 0.9708588957055214,
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+ "f1-score": 0.9461883408071748,
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+ "support": 652.0
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+ },
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+ "accuracy": 0.9476363636363636,
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+ "macro avg": {
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+ "precision": 0.9475821636969115,
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+ "recall": 0.9487766124447385,
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+ "f1-score": 0.9475984196953722,
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+ "support": 1375.0
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+ },
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+ "weighted avg": {
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+ "precision": 0.9488648955972792,
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+ "recall": 0.9476363636363636,
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+ "f1-score": 0.9476712310415989,
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+ "support": 1375.0
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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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+ | "Let’s discuss how we can support Amir’s promotion to the regional director role, ensuring clients in conservative markets understand our commitment to diversity and inclusion—it’s an opportunity to lead by example." | PASS | PASS |
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+ | "To Facilities: Can we install a small secure storage unit in the IT server room? I’d like a designated place to store sensitive equipment during after-hours work to ensure compliance with workplace safety policies." | PASS | PASS |
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+ | **Internal Wiki Edit**: "Troubleshooting guide update: If your device gets quarantined for ‘suspicious activity’, immediately contact IT for approval before taking any action. Unauthorized factory resets may violate security policies." | PASS | PASS |
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+ | **Casual Desk Chat**: "My weekend gig as a freelance compliance auditor for BankCorp pays better than overtime here. Shh, though—management ‘wouldn’t understand’." | FAIL | FAIL |
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+ | "Hey Sarah, I noticed you’ve been staying late a lot. How about we grab a drink at my place after work tonight? Don’t worry—I’ll make sure the VP hears about your ‘dedication’ if you’re open to keeping things between us." *(Email from senior engineer to junior colleague)* | FAIL | FAIL |
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+ | "Let’s discuss how we can support Amir’s promotion to the regional director role, ensuring clients in conservative markets understand our commitment to diversity and inclusion—it’s an opportunity to lead by example." | PASS | PASS |
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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 | 0.0003 | 3472.11 |
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+ | 1000 | 0.078 | 12822.97 |
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+ | 1375 | 0.1002 | 13720.64 |
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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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+ | general-safety-education-binary | [enguard/tiny-guard-2m-en-general-safety-education-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-general-safety-education-binary-guardset) | 0.9672 | 0.9117 | 0.9386 |
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+ | general-safety-hr-binary | [enguard/tiny-guard-2m-en-general-safety-hr-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-general-safety-hr-binary-guardset) | 0.9643 | 0.8976 | 0.9298 |
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+ | general-safety-social-media-binary | [enguard/tiny-guard-2m-en-general-safety-social-media-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-general-safety-social-media-binary-guardset) | 0.9484 | 0.8814 | 0.9137 |
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+ | prompt-response-safety-binary | [enguard/tiny-guard-2m-en-prompt-response-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-response-safety-binary-guardset) | 0.9514 | 0.8627 | 0.9049 |
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+ | prompt-safety-binary | [enguard/tiny-guard-2m-en-prompt-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-safety-binary-guardset) | 0.9564 | 0.8965 | 0.9255 |
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+ | prompt-safety-cyber-binary | [enguard/tiny-guard-2m-en-prompt-safety-cyber-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-safety-cyber-binary-guardset) | 0.9540 | 0.8316 | 0.8886 |
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+ | prompt-safety-finance-binary | [enguard/tiny-guard-2m-en-prompt-safety-finance-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-safety-finance-binary-guardset) | 0.9939 | 0.9819 | 0.9878 |
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+ | prompt-safety-law-binary | [enguard/tiny-guard-2m-en-prompt-safety-law-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-safety-law-binary-guardset) | 0.9783 | 0.8824 | 0.9278 |
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+ | response-safety-binary | [enguard/tiny-guard-2m-en-response-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-response-safety-binary-guardset) | 0.9338 | 0.8098 | 0.8674 |
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+ | response-safety-cyber-binary | [enguard/tiny-guard-2m-en-response-safety-cyber-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-response-safety-cyber-binary-guardset) | 0.9623 | 0.7907 | 0.8681 |
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+ | response-safety-finance-binary | [enguard/tiny-guard-2m-en-response-safety-finance-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-response-safety-finance-binary-guardset) | 0.9350 | 0.8409 | 0.8855 |
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+ | response-safety-law-binary | [enguard/tiny-guard-2m-en-response-safety-law-binary-guardset](https://huggingface.co/enguard/tiny-guard-2m-en-response-safety-law-binary-guardset) | 0.9344 | 0.7215 | 0.8143 |
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+ | general-safety-education-binary | [enguard/tiny-guard-4m-en-general-safety-education-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-general-safety-education-binary-guardset) | 0.9760 | 0.8985 | 0.9356 |
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+ | general-safety-hr-binary | [enguard/tiny-guard-4m-en-general-safety-hr-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-general-safety-hr-binary-guardset) | 0.9724 | 0.9267 | 0.9490 |
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+ | general-safety-social-media-binary | [enguard/tiny-guard-4m-en-general-safety-social-media-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-general-safety-social-media-binary-guardset) | 0.9651 | 0.9212 | 0.9427 |
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+ | prompt-response-safety-binary | [enguard/tiny-guard-4m-en-prompt-response-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-response-safety-binary-guardset) | 0.9783 | 0.8769 | 0.9249 |
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+ | prompt-safety-binary | [enguard/tiny-guard-4m-en-prompt-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-safety-binary-guardset) | 0.9632 | 0.9137 | 0.9378 |
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+ | prompt-safety-cyber-binary | [enguard/tiny-guard-4m-en-prompt-safety-cyber-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-safety-cyber-binary-guardset) | 0.9570 | 0.8930 | 0.9239 |
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+ | prompt-safety-finance-binary | [enguard/tiny-guard-4m-en-prompt-safety-finance-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-safety-finance-binary-guardset) | 0.9939 | 0.9819 | 0.9878 |
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+ | prompt-safety-law-binary | [enguard/tiny-guard-4m-en-prompt-safety-law-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-safety-law-binary-guardset) | 0.9898 | 0.9510 | 0.9700 |
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+ | response-safety-binary | [enguard/tiny-guard-4m-en-response-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-response-safety-binary-guardset) | 0.9414 | 0.8345 | 0.8847 |
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+ | response-safety-cyber-binary | [enguard/tiny-guard-4m-en-response-safety-cyber-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-response-safety-cyber-binary-guardset) | 0.9588 | 0.8424 | 0.8968 |
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+ | response-safety-finance-binary | [enguard/tiny-guard-4m-en-response-safety-finance-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-response-safety-finance-binary-guardset) | 0.9536 | 0.8669 | 0.9082 |
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+ | response-safety-law-binary | [enguard/tiny-guard-4m-en-response-safety-law-binary-guardset](https://huggingface.co/enguard/tiny-guard-4m-en-response-safety-law-binary-guardset) | 0.8983 | 0.6709 | 0.7681 |
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+ | general-safety-education-binary | [enguard/tiny-guard-8m-en-general-safety-education-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-general-safety-education-binary-guardset) | 0.9790 | 0.9249 | 0.9512 |
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+ | general-safety-hr-binary | [enguard/tiny-guard-8m-en-general-safety-hr-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-general-safety-hr-binary-guardset) | 0.9810 | 0.9267 | 0.9531 |
160
+ | general-safety-social-media-binary | [enguard/tiny-guard-8m-en-general-safety-social-media-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-general-safety-social-media-binary-guardset) | 0.9793 | 0.9102 | 0.9435 |
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+ | prompt-response-safety-binary | [enguard/tiny-guard-8m-en-prompt-response-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-response-safety-binary-guardset) | 0.9753 | 0.9197 | 0.9467 |
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+ | prompt-safety-binary | [enguard/tiny-guard-8m-en-prompt-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-safety-binary-guardset) | 0.9731 | 0.8876 | 0.9284 |
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+ | prompt-safety-cyber-binary | [enguard/tiny-guard-8m-en-prompt-safety-cyber-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-safety-cyber-binary-guardset) | 0.9649 | 0.8824 | 0.9218 |
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+ | prompt-safety-finance-binary | [enguard/tiny-guard-8m-en-prompt-safety-finance-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-safety-finance-binary-guardset) | 0.9939 | 0.9849 | 0.9894 |
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+ | prompt-safety-law-binary | [enguard/tiny-guard-8m-en-prompt-safety-law-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-safety-law-binary-guardset) | 1.0000 | 0.9412 | 0.9697 |
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+ | response-safety-binary | [enguard/tiny-guard-8m-en-response-safety-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-response-safety-binary-guardset) | 0.9407 | 0.8687 | 0.9033 |
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+ | response-safety-cyber-binary | [enguard/tiny-guard-8m-en-response-safety-cyber-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-response-safety-cyber-binary-guardset) | 0.9626 | 0.8656 | 0.9116 |
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+ | response-safety-finance-binary | [enguard/tiny-guard-8m-en-response-safety-finance-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-response-safety-finance-binary-guardset) | 0.9516 | 0.8929 | 0.9213 |
169
+ | response-safety-law-binary | [enguard/tiny-guard-8m-en-response-safety-law-binary-guardset](https://huggingface.co/enguard/tiny-guard-8m-en-response-safety-law-binary-guardset) | 0.8955 | 0.7595 | 0.8219 |
170
+ | general-safety-education-binary | [enguard/small-guard-32m-en-general-safety-education-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-general-safety-education-binary-guardset) | 0.9835 | 0.9183 | 0.9498 |
171
+ | general-safety-hr-binary | [enguard/small-guard-32m-en-general-safety-hr-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-general-safety-hr-binary-guardset) | 0.9868 | 0.9322 | 0.9587 |
172
+ | general-safety-social-media-binary | [enguard/small-guard-32m-en-general-safety-social-media-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-general-safety-social-media-binary-guardset) | 0.9783 | 0.9300 | 0.9535 |
173
+ | prompt-response-safety-binary | [enguard/small-guard-32m-en-prompt-response-safety-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-prompt-response-safety-binary-guardset) | 0.9715 | 0.9288 | 0.9497 |
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+ | prompt-safety-binary | [enguard/small-guard-32m-en-prompt-safety-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-prompt-safety-binary-guardset) | 0.9730 | 0.9284 | 0.9502 |
175
+ | prompt-safety-cyber-binary | [enguard/small-guard-32m-en-prompt-safety-cyber-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-prompt-safety-cyber-binary-guardset) | 0.9490 | 0.8957 | 0.9216 |
176
+ | prompt-safety-finance-binary | [enguard/small-guard-32m-en-prompt-safety-finance-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-prompt-safety-finance-binary-guardset) | 1.0000 | 0.9879 | 0.9939 |
177
+ | prompt-safety-law-binary | [enguard/small-guard-32m-en-prompt-safety-law-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-prompt-safety-law-binary-guardset) | 1.0000 | 0.9314 | 0.9645 |
178
+ | response-safety-binary | [enguard/small-guard-32m-en-response-safety-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-response-safety-binary-guardset) | 0.9484 | 0.8550 | 0.8993 |
179
+ | response-safety-cyber-binary | [enguard/small-guard-32m-en-response-safety-cyber-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-response-safety-cyber-binary-guardset) | 0.9681 | 0.8630 | 0.9126 |
180
+ | response-safety-finance-binary | [enguard/small-guard-32m-en-response-safety-finance-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-response-safety-finance-binary-guardset) | 0.9650 | 0.8961 | 0.9293 |
181
+ | response-safety-law-binary | [enguard/small-guard-32m-en-response-safety-law-binary-guardset](https://huggingface.co/enguard/small-guard-32m-en-response-safety-law-binary-guardset) | 0.9298 | 0.6709 | 0.7794 |
182
+ | general-safety-education-binary | [enguard/medium-guard-128m-xx-general-safety-education-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-general-safety-education-binary-guardset) | 0.9806 | 0.8918 | 0.9341 |
183
+ | general-safety-hr-binary | [enguard/medium-guard-128m-xx-general-safety-hr-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-general-safety-hr-binary-guardset) | 0.9865 | 0.9129 | 0.9483 |
184
+ | general-safety-social-media-binary | [enguard/medium-guard-128m-xx-general-safety-social-media-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-general-safety-social-media-binary-guardset) | 0.9690 | 0.9452 | 0.9570 |
185
+ | prompt-response-safety-binary | [enguard/medium-guard-128m-xx-prompt-response-safety-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-response-safety-binary-guardset) | 0.9595 | 0.9197 | 0.9392 |
186
+ | prompt-safety-binary | [enguard/medium-guard-128m-xx-prompt-safety-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-safety-binary-guardset) | 0.9676 | 0.9321 | 0.9495 |
187
+ | prompt-safety-cyber-binary | [enguard/medium-guard-128m-xx-prompt-safety-cyber-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-safety-cyber-binary-guardset) | 0.9558 | 0.8663 | 0.9088 |
188
+ | prompt-safety-finance-binary | [enguard/medium-guard-128m-xx-prompt-safety-finance-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-safety-finance-binary-guardset) | 1.0000 | 0.9909 | 0.9954 |
189
+ | prompt-safety-law-binary | [enguard/medium-guard-128m-xx-prompt-safety-law-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-safety-law-binary-guardset) | 0.9890 | 0.8824 | 0.9326 |
190
+ | response-safety-binary | [enguard/medium-guard-128m-xx-response-safety-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-response-safety-binary-guardset) | 0.9279 | 0.8632 | 0.8944 |
191
+ | response-safety-cyber-binary | [enguard/medium-guard-128m-xx-response-safety-cyber-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-response-safety-cyber-binary-guardset) | 0.9607 | 0.8837 | 0.9206 |
192
+ | response-safety-finance-binary | [enguard/medium-guard-128m-xx-response-safety-finance-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-response-safety-finance-binary-guardset) | 0.9381 | 0.8864 | 0.9115 |
193
+ | response-safety-law-binary | [enguard/medium-guard-128m-xx-response-safety-law-binary-guardset](https://huggingface.co/enguard/medium-guard-128m-xx-response-safety-law-binary-guardset) | 0.9194 | 0.7215 | 0.8085 |
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
200
 
201
  ## Citation
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203
+ 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},