ColBERTv1.0-bert-based-spanish-mmarcoES

New spanish ColBERTv2 model available here

Training

Details

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).