Text Classification
Transformers
Safetensors
xlm-roberta
news
bias
nlp
mediabias
multilingual
text-embeddings-inference
Instructions to use himel7/multilingual-bias-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use himel7/multilingual-bias-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="himel7/multilingual-bias-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("himel7/multilingual-bias-detector") model = AutoModelForSequenceClassification.from_pretrained("himel7/multilingual-bias-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -35,6 +35,8 @@ base_model:
|
|
| 35 |
|
| 36 |
A model that detects biases in news texts in several languages.
|
| 37 |
|
|
|
|
|
|
|
| 38 |
|
| 39 |
|
| 40 |
## Model Details
|
|
|
|
| 35 |
|
| 36 |
A model that detects biases in news texts in several languages.
|
| 37 |
|
| 38 |
+
This model is distilled from himel7/bias-detector model on a xlm-roberta (multilingual) model base using Transfer Learning.
|
| 39 |
+
|
| 40 |
|
| 41 |
|
| 42 |
## Model Details
|