Hugging Face's logo

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

Text Classification
Model2Vec
Safetensors
static-embeddings
Model card Files
xet
Community
davidberenstein1957 commited on
Commit
c2b9a6d
·
verified ·
1 Parent(s): 26a5f8e

Upload README.md with huggingface_hub

Browse files
Files changed (1) hide show
  1. README.md +151 -61
README.md CHANGED
@@ -1,92 +1,182 @@
1
  ---
2
- base_model: unknown
 
 
3
  library_name: model2vec
4
  license: mit
5
- model_name: tmp3_brajt3
6
  tags:
7
- - embeddings
8
  - static-embeddings
9
- - sentence-transformers
 
10
  ---
11
 
12
- # tmp3_brajt3 Model Card
 
 
13
 
14
- 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.
15
 
16
 
17
  ## Installation
18
 
19
- Install model2vec using pip:
20
- ```
21
- pip install model2vec
22
  ```
23
 
24
  ## Usage
25
 
26
- ### Using Model2Vec
27
-
28
- The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
29
-
30
- Load this model using the `from_pretrained` method:
31
  ```python
32
- from model2vec import StaticModel
33
-
34
- # Load a pretrained Model2Vec model
35
- model = StaticModel.from_pretrained("tmp3_brajt3")
36
-
37
- # Compute text embeddings
38
- embeddings = model.encode(["Example sentence"])
39
- ```
40
 
41
- ### Using Sentence Transformers
 
 
42
 
43
- You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
44
 
45
- ```python
46
- from sentence_transformers import SentenceTransformer
47
 
48
- # Load a pretrained Sentence Transformer model
49
- model = SentenceTransformer("tmp3_brajt3")
50
 
51
- # Compute text embeddings
52
- embeddings = model.encode(["Example sentence"])
53
  ```
54
 
55
- ### Distilling a Model2Vec model
56
-
57
- 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:
58
-
59
- ```python
60
- from model2vec.distill import distill
61
-
62
- # Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
63
- m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
64
-
65
- # Save the model
66
- m2v_model.save_pretrained("m2v_model")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
67
  ```
68
-
69
- ## How it works
70
-
71
- 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.
72
-
73
- 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.
74
-
75
- ## Additional Resources
76
-
77
- - [Model2Vec Repo](https://github.com/MinishLab/model2vec)
78
- - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
79
- - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
80
- - [Model2Vec Docs](https://minish.ai/packages/model2vec/introduction)
81
-
82
-
83
- ## Library Authors
84
-
85
- 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).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
86
 
87
  ## Citation
88
 
89
- Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
 
90
  ```
91
  @software{minishlab2024model2vec,
92
  author = {Stephan Tulkens and {van Dongen}, Thomas},
 
1
  ---
2
+ base_model: minishlab/potion-base-4m
3
+ datasets:
4
+ - enguard/multi-lingual-prompt-moderation
5
  library_name: model2vec
6
  license: mit
7
+ model_name: enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation
8
  tags:
 
9
  - static-embeddings
10
+ - text-classification
11
+ - model2vec
12
  ---
13
 
14
+ # enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation
15
+
16
+ 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.
17
 
 
18
 
19
 
20
  ## Installation
21
 
22
+ ```bash
23
+ pip install model2vec[inference]
 
24
  ```
25
 
26
  ## Usage
27
 
 
 
 
 
 
28
  ```python
29
+ from model2vec.inference import StaticModelPipeline
 
 
 
 
 
 
 
30
 
31
+ model = StaticModelPipeline.from_pretrained(
32
+ "enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation"
33
+ )
34
 
 
35
 
36
+ # Supports single texts. Format input as a single text:
37
+ text = "Example sentence"
38
 
39
+ model.predict([text])
40
+ model.predict_proba([text])
41
 
 
 
42
  ```
43
 
44
+ ## Why should you use these models?
45
+
46
+ - Optimized for precision to reduce false positives.
47
+ - Extremely fast inference: up to x500 faster than SetFit.
48
+
49
+ ## This model variant
50
+
51
+ Below is a quick overview of the model variant and core metrics.
52
+
53
+ | Field | Value |
54
+ |---|---|
55
+ | Classifies | prompt-harmfulness-binary |
56
+ | Base Model | [minishlab/potion-base-4m](https://huggingface.co/minishlab/potion-base-4m) |
57
+ | Precision | 0.8565 |
58
+ | Recall | 0.7540 |
59
+ | F1 | 0.8020 |
60
+
61
+ ### Confusion Matrix
62
+
63
+ | True \ Predicted | FAIL | PASS |
64
+ | --- | --- | --- |
65
+ | **FAIL** | 2050 | 676 |
66
+ | **PASS** | 341 | 2385 |
67
+
68
+ <details>
69
+ <summary><b>Full metrics (JSON)</b></summary>
70
+
71
+ ```json
72
+ {
73
+ "FAIL": {
74
+ "precision": 0.8564853556485356,
75
+ "recall": 0.7539594843462247,
76
+ "f1-score": 0.8019588638589618,
77
+ "support": 2715.0
78
+ },
79
+ "PASS": {
80
+ "precision": 0.7792465300727033,
81
+ "recall": 0.8730099962976675,
82
+ "f1-score": 0.8234677841801991,
83
+ "support": 2701.0
84
+ },
85
+ "accuracy": 0.8133308714918759,
86
+ "macro avg": {
87
+ "precision": 0.8178659428606194,
88
+ "recall": 0.8134847403219461,
89
+ "f1-score": 0.8127133240195804,
90
+ "support": 5416.0
91
+ },
92
+ "weighted avg": {
93
+ "precision": 0.8179657714756546,
94
+ "recall": 0.8133308714918759,
95
+ "f1-score": 0.812685524454911,
96
+ "support": 5416.0
97
+ }
98
+ }
99
  ```
100
+ </details>
101
+
102
+
103
+ <details>
104
+ <summary><b>Sample Predictions</b></summary>
105
+
106
+ | Text | True Label | Predicted Label |
107
+ |------|------------|-----------------|
108
+ | 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 |
109
+ | I'm surprised they let you stay without buying anything. | PASS | PASS |
110
+ | 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 |
111
+ | Seriously?<br><br>You're "depressed" because of a story? Oh, Reddit. | PASS | PASS |
112
+ | Yeah what kind of community manager publicly mocks their players achievements? With no clarification, straight douche. | FAIL | FAIL |
113
+ | 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 |
114
+ </details>
115
+
116
+
117
+ <details>
118
+ <summary><b>Prediction Speed Benchmarks</b></summary>
119
+
120
+ | Dataset Size | Time (seconds) | Predictions/Second |
121
+ |--------------|----------------|---------------------|
122
+ | 1 | 0.0002 | 4485.89 |
123
+ | 1000 | 0.0614 | 16280.53 |
124
+ | 5452 | 0.2854 | 19103.34 |
125
+ </details>
126
+
127
+
128
+ ## Other model variants
129
+
130
+ Below is a general overview of the best-performing models for each dataset variant.
131
+
132
+ | Classifies | Model | Precision | Recall | F1 |
133
+ | --- | --- | --- | --- | --- |
134
+ | prompt-harassment-binary | [enguard/tiny-guard-2m-en-prompt-harassment-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-harassment-binary-moderation) | 0.8788 | 0.7180 | 0.7903 |
135
+ | prompt-harmfulness-binary | [enguard/tiny-guard-2m-en-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-harmfulness-binary-moderation) | 0.8543 | 0.7256 | 0.7847 |
136
+ | prompt-harmfulness-multilabel | [enguard/tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation) | 0.7687 | 0.5006 | 0.6064 |
137
+ | 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) | 0.9141 | 0.7269 | 0.8098 |
138
+ | 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 |
139
+ | 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) | 0.9256 | 0.8141 | 0.8663 |
140
+ | prompt-violence-binary | [enguard/tiny-guard-2m-en-prompt-violence-binary-moderation](https://huggingface.co/enguard/tiny-guard-2m-en-prompt-violence-binary-moderation) | 0.9017 | 0.7645 | 0.8275 |
141
+ | prompt-harassment-binary | [enguard/tiny-guard-4m-en-prompt-harassment-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-harassment-binary-moderation) | 0.8895 | 0.7160 | 0.7934 |
142
+ | prompt-harmfulness-binary | [enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation) | 0.8565 | 0.7540 | 0.8020 |
143
+ | prompt-harmfulness-multilabel | [enguard/tiny-guard-4m-en-prompt-harmfulness-multilabel-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-harmfulness-multilabel-moderation) | 0.7924 | 0.5663 | 0.6606 |
144
+ | 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) | 0.9198 | 0.7831 | 0.8460 |
145
+ | 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 |
146
+ | 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) | 0.9371 | 0.8468 | 0.8897 |
147
+ | prompt-violence-binary | [enguard/tiny-guard-4m-en-prompt-violence-binary-moderation](https://huggingface.co/enguard/tiny-guard-4m-en-prompt-violence-binary-moderation) | 0.8851 | 0.8370 | 0.8603 |
148
+ | prompt-harassment-binary | [enguard/tiny-guard-8m-en-prompt-harassment-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-harassment-binary-moderation) | 0.8895 | 0.7767 | 0.8292 |
149
+ | prompt-harmfulness-binary | [enguard/tiny-guard-8m-en-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-harmfulness-binary-moderation) | 0.8627 | 0.7912 | 0.8254 |
150
+ | prompt-harmfulness-multilabel | [enguard/tiny-guard-8m-en-prompt-harmfulness-multilabel-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-harmfulness-multilabel-moderation) | 0.7902 | 0.5926 | 0.6773 |
151
+ | 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) | 0.9152 | 0.8233 | 0.8668 |
152
+ | 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 |
153
+ | 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) | 0.9382 | 0.8881 | 0.9125 |
154
+ | prompt-violence-binary | [enguard/tiny-guard-8m-en-prompt-violence-binary-moderation](https://huggingface.co/enguard/tiny-guard-8m-en-prompt-violence-binary-moderation) | 0.9042 | 0.8551 | 0.8790 |
155
+ | prompt-harassment-binary | [enguard/small-guard-32m-en-prompt-harassment-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-harassment-binary-moderation) | 0.8809 | 0.7964 | 0.8365 |
156
+ | prompt-harmfulness-binary | [enguard/small-guard-32m-en-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-harmfulness-binary-moderation) | 0.8548 | 0.8239 | 0.8391 |
157
+ | prompt-harmfulness-multilabel | [enguard/small-guard-32m-en-prompt-harmfulness-multilabel-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-harmfulness-multilabel-moderation) | 0.8065 | 0.6494 | 0.7195 |
158
+ | 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) | 0.9207 | 0.8394 | 0.8782 |
159
+ | 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 |
160
+ | 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) | 0.9328 | 0.8847 | 0.9081 |
161
+ | prompt-violence-binary | [enguard/small-guard-32m-en-prompt-violence-binary-moderation](https://huggingface.co/enguard/small-guard-32m-en-prompt-violence-binary-moderation) | 0.9077 | 0.8913 | 0.8995 |
162
+ | prompt-harassment-binary | [enguard/medium-guard-128m-xx-prompt-harassment-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-harassment-binary-moderation) | 0.8660 | 0.8034 | 0.8336 |
163
+ | prompt-harmfulness-binary | [enguard/medium-guard-128m-xx-prompt-harmfulness-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-harmfulness-binary-moderation) | 0.8457 | 0.8074 | 0.8261 |
164
+ | prompt-harmfulness-multilabel | [enguard/medium-guard-128m-xx-prompt-harmfulness-multilabel-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-harmfulness-multilabel-moderation) | 0.7795 | 0.6516 | 0.7098 |
165
+ | 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) | 0.8826 | 0.8153 | 0.8476 |
166
+ | 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 |
167
+ | 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) | 0.9153 | 0.8744 | 0.8944 |
168
+ | prompt-violence-binary | [enguard/medium-guard-128m-xx-prompt-violence-binary-moderation](https://huggingface.co/enguard/medium-guard-128m-xx-prompt-violence-binary-moderation) | 0.8821 | 0.8406 | 0.8609 |
169
+
170
+ ## Resources
171
+
172
+ - Awesome AI Guardrails: <https://github.com/enguard-ai/awesome-ai-guardails>
173
+ - Model2Vec: https://github.com/MinishLab/model2vec
174
+ - Docs: https://minish.ai/packages/model2vec/introduction
175
 
176
  ## Citation
177
 
178
+ If you use this model, please cite Model2Vec:
179
+
180
  ```
181
  @software{minishlab2024model2vec,
182
  author = {Stephan Tulkens and {van Dongen}, Thomas},