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
|
@@ -7,7 +7,7 @@ datasets:
|
|
| 7 |
---
|
| 8 |
|
| 9 |
# Data
|
| 10 |
-
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs
|
| 11 |
|
| 12 |
## Model
|
| 13 |
model created by [sentence-tansformers](https://www.sbert.net/index.html),model struct is cross-encoder
|
|
|
|
| 7 |
---
|
| 8 |
|
| 9 |
# Data
|
| 10 |
+
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.
|
| 11 |
|
| 12 |
## Model
|
| 13 |
model created by [sentence-tansformers](https://www.sbert.net/index.html),model struct is cross-encoder
|