Instructions to use DAMO-NLP-SG/zero-shot-classify-SSTuning-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/zero-shot-classify-SSTuning-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="DAMO-NLP-SG/zero-shot-classify-SSTuning-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/zero-shot-classify-SSTuning-base") model = AutoModelForSequenceClassification.from_pretrained("DAMO-NLP-SG/zero-shot-classify-SSTuning-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -70,7 +70,7 @@ print(predictions)
|
|
| 70 |
Chip Hong Chang and
|
| 71 |
Lidong Bing},
|
| 72 |
title = {Zero-Shot Text Classification via Self-Supervised Tuning},
|
| 73 |
-
booktitle={Findings of the 2023 ACL},
|
| 74 |
year = {2023},
|
| 75 |
url = {},
|
| 76 |
}
|
|
|
|
| 70 |
Chip Hong Chang and
|
| 71 |
Lidong Bing},
|
| 72 |
title = {Zero-Shot Text Classification via Self-Supervised Tuning},
|
| 73 |
+
booktitle = {Findings of the 2023 ACL},
|
| 74 |
year = {2023},
|
| 75 |
url = {},
|
| 76 |
}
|