Instructions to use tuhailong/cross-encoder-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuhailong/cross-encoder-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tuhailong/cross-encoder-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tuhailong/cross-encoder-bert-base") model = AutoModelForSequenceClassification.from_pretrained("tuhailong/cross-encoder-bert-base") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -18,7 +18,4 @@ model created by [sentence-tansformers](https://www.sbert.net/index.html),mode
|
|
| 18 |
>>> model = CrossEncoder('tuhailong/cross-encoder')
|
| 19 |
>>> scores = model.predict([["今天天气不错", "今天心情不错"]])
|
| 20 |
>>> print(scores)
|
| 21 |
-
```
|
| 22 |
-
|
| 23 |
-
#### Code
|
| 24 |
-
train code from https://github.com/TTurn/cross-encoder
|
|
|
|
| 18 |
>>> model = CrossEncoder('tuhailong/cross-encoder')
|
| 19 |
>>> scores = model.predict([["今天天气不错", "今天心情不错"]])
|
| 20 |
>>> print(scores)
|
| 21 |
+
```
|
|
|
|
|
|
|
|
|