Text Classification
Transformers
Safetensors
English
distilbert
content-moderation
comment-moderation
text-moderation
text-embeddings-inference
Instructions to use Vrandan/Comment-Moderation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vrandan/Comment-Moderation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vrandan/Comment-Moderation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vrandan/Comment-Moderation") model = AutoModelForSequenceClassification.from_pretrained("Vrandan/Comment-Moderation") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -15,7 +15,7 @@ base_model:
|
|
| 15 |
|
| 16 |
[](https://huggingface.co/Vrandan/Comment-Moderation)
|
| 17 |
[](https://www.python.org/downloads/release/python-370/)
|
| 18 |
-
[](https://
|
| 19 |
|
| 20 |
|
| 21 |
A powerful, multi-label content moderation system built on **DistilBERT** architecture, designed to detect and classify potentially harmful content in user-generated comments with high accuracy. This model stands out as currently the best in terms of performance based on the provided dataset for text moderation. Additionally, it has the smallest footprint, making it ideal for deployment on edge devices. Currently, it is the only model trained to achieve such high performance while maintaining a minimal size relative to the training data on Hugging Face.
|
|
|
|
| 15 |
|
| 16 |
[](https://huggingface.co/Vrandan/Comment-Moderation)
|
| 17 |
[](https://www.python.org/downloads/release/python-370/)
|
| 18 |
+
[](https://huggingface.co/Vrandan/Comment-Moderation/blob/main/Comment%20Moderation-OpenRAIL.md)
|
| 19 |
|
| 20 |
|
| 21 |
A powerful, multi-label content moderation system built on **DistilBERT** architecture, designed to detect and classify potentially harmful content in user-generated comments with high accuracy. This model stands out as currently the best in terms of performance based on the provided dataset for text moderation. Additionally, it has the smallest footprint, making it ideal for deployment on edge devices. Currently, it is the only model trained to achieve such high performance while maintaining a minimal size relative to the training data on Hugging Face.
|