Text Classification
Transformers
PyTorch
Dutch
bert
text classification
sentiment analysis
domain adaptation
text-embeddings-inference
Instructions to use clips/republic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clips/republic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="clips/republic")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clips/republic") model = AutoModelForSequenceClassification.from_pretrained("clips/republic") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -27,7 +27,7 @@ RePublic (<u>re</u>putation analyzer for <u>public</u> service organizations) is
|
|
| 27 |
### How to use
|
| 28 |
The model can be loaded and used to make predictions as follows:
|
| 29 |
|
| 30 |
-
```
|
| 31 |
from transformers import pipeline
|
| 32 |
model_path = 'clips/republic'
|
| 33 |
pipe = pipeline(task="text-classification",
|
|
|
|
| 27 |
### How to use
|
| 28 |
The model can be loaded and used to make predictions as follows:
|
| 29 |
|
| 30 |
+
```python
|
| 31 |
from transformers import pipeline
|
| 32 |
model_path = 'clips/republic'
|
| 33 |
pipe = pipeline(task="text-classification",
|