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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
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# T5-ANCE
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.
Dataset used for training:
- MS MARCO Passage
Evaluation result:
|Dataset|Metric|Result|
|---|---|---|
|MS MARCO Passage (dev) | MRR@10 | 0.3570|
Important hyper-parameters:
|Name|Value|
|---|---|
|Global batch size|256|
|Learning rate|5e-6|
|Maximum length of query|32|
|Maximum length of document|128|
|Template for query|`<text>`|
|Template for document|`Title: <title> Text: <text>`|
### Paper
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