Instructions to use Shenzy/Sentence_Classification4DesignTutor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shenzy/Sentence_Classification4DesignTutor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shenzy/Sentence_Classification4DesignTutor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shenzy/Sentence_Classification4DesignTutor") model = AutoModelForSequenceClassification.from_pretrained("Shenzy/Sentence_Classification4DesignTutor") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -2,7 +2,7 @@
|
|
| 2 |
tags: autotrain
|
| 3 |
language: en
|
| 4 |
widget:
|
| 5 |
-
- text: "
|
| 6 |
datasets:
|
| 7 |
- Shenzy/autotrain-data-sentence_classification
|
| 8 |
co2_eq_emissions: 0.00986494387043499
|
|
|
|
| 2 |
tags: autotrain
|
| 3 |
language: en
|
| 4 |
widget:
|
| 5 |
+
- text: "An unusual hierarchy in the section near the top where the design seems to prioritise running time over a compacted artist name."
|
| 6 |
datasets:
|
| 7 |
- Shenzy/autotrain-data-sentence_classification
|
| 8 |
co2_eq_emissions: 0.00986494387043499
|