Instructions to use OpenMatch/t5-ance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMatch/t5-ance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OpenMatch/t5-ance")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OpenMatch/t5-ance") model = AutoModel.from_pretrained("OpenMatch/t5-ance") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -4,7 +4,7 @@ license: mit
|
|
| 4 |
|
| 5 |
# T5-ANCE
|
| 6 |
|
| 7 |
-
T5-ANCE generally follows the training procedure described in [this page](dr-msmarco-passage.
|
| 8 |
|
| 9 |
Dataset used for training:
|
| 10 |
- MS MARCO Passage
|
|
|
|
| 4 |
|
| 5 |
# T5-ANCE
|
| 6 |
|
| 7 |
+
T5-ANCE generally follows the training procedure described in [this page](https://openmatch.readthedocs.io/en/latest/dr-msmarco-passage.html), but uses a much larger batch size.
|
| 8 |
|
| 9 |
Dataset used for training:
|
| 10 |
- MS MARCO Passage
|