Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Cross-lingual-nlp
zero-shot-transfer
toxicity-analysis
abuse-detection
flag-user
block-user
multilinguality
XLM-R
Instructions to use Jayveersinh-Raj/PolyGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jayveersinh-Raj/PolyGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jayveersinh-Raj/PolyGuard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jayveersinh-Raj/PolyGuard") model = AutoModelForSequenceClassification.from_pretrained("Jayveersinh-Raj/PolyGuard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
b1a65cc
1
Parent(s): b225c15
Update README.md
Browse files
README.md
CHANGED
|
@@ -228,7 +228,7 @@ The model better than GPT4, and a human annotator that annotated the comments of
|
|
| 228 |
### Testing Data, Factors & Metrics
|
| 229 |
1. Tested on human annotations
|
| 230 |
2. Tested on GPT4 generated texts
|
| 231 |
-
3. F1-score on english test set
|
| 232 |
|
| 233 |
#### Testing Data
|
| 234 |
|
|
|
|
| 228 |
### Testing Data, Factors & Metrics
|
| 229 |
1. Tested on human annotations
|
| 230 |
2. Tested on GPT4 generated texts
|
| 231 |
+
3. F1-score on english test set → 0.96.
|
| 232 |
|
| 233 |
#### Testing Data
|
| 234 |
|