# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("OpenMatch/t5-ance")
model = AutoModel.from_pretrained("OpenMatch/t5-ance")Quick Links
T5-ANCE
T5-ANCE generally follows the training procedure described in this page, 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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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OpenMatch/t5-ance")