The model is initialized from the
bert-base-spanish-wwm-uncased
checkpoint and fine-tuned on 10M triples via pairwise softmax cross-entropy loss over the computed scores of the positive and negative passages associated to a query. It was trained on a single Tesla A100 GPU with 40GBs of memory during 200k steps with 10% of warmup steps using a batch size of 96 and the AdamW optimizer with a constant learning rate of 3e-06. Total training time was around 12 hours.
Data
The model is fine-tuned on the Spanish version of the
mMARCO
dataset, a multi-lingual machine-translated version of the MS MARCO dataset.
The triples are sampled from the ~39.8M triples of
triples.train.small.tsv
Evaluation
The model is evaluated on the smaller development set of mMARCO-es, which consists of 6,980 queries for a corpus of 8.8M candidate passages. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k).
model
Vocab.
#Param.
Size
MRR@10
R@50
R@1000
ColBERTv1.0-bert-based-spanish-mmarcoES
spanish
110M
440MB
24.70
59,23
63.86
Runs of AdrienB134 ColBERTv1.0-bert-based-spanish-mmarcoES on huggingface.co
17
Total runs
1
24-hour runs
1
3-day runs
3
7-day runs
10
30-day runs
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