Instructions to use pin/senda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pin/senda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pin/senda")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pin/senda") model = AutoModelForSequenceClassification.from_pretrained("pin/senda", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
c3e7d66
1
Parent(s): b478740
small improvements
Browse files
README.md
CHANGED
|
@@ -26,8 +26,8 @@ model = AutoModelForSequenceClassification.from_pretrained("pin/senda")
|
|
| 26 |
senda_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
|
| 27 |
|
| 28 |
text = "Sikke en dejlig dag det er i dag"
|
| 29 |
-
# 'what a lovely day'
|
| 30 |
-
senda_pipeline(
|
| 31 |
```
|
| 32 |
|
| 33 |
#### Contact
|
|
|
|
| 26 |
senda_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
|
| 27 |
|
| 28 |
text = "Sikke en dejlig dag det er i dag"
|
| 29 |
+
# in English: 'what a lovely day'
|
| 30 |
+
senda_pipeline(text)
|
| 31 |
```
|
| 32 |
|
| 33 |
#### Contact
|