This is an updated version of the anydef model. The primary goal was to use an improved dataset during fine-tuning, enabling the model to better understand nuances. Overall, anydef-v2 offers better performance in benchmarks, and manual inspection of the results suggests that the model has indeed improved.

Precision (%):

Dataset	anydef	anydef-v2
RSS-500	66.23	66.89
ISTEX-1000	86.72	85.82
Reuters-128	63.8	64.88
TweekiGold	75.23	75.93

Retrieval rate (%):

Dataset	anydef	anydef-v2
RSS-500	82.78	84.11
ISTEX-1000	97.91	97.76
Reuters-128	80.47	83.33
TweekiGold	89.93	91.67

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: inverse_sqrt
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Framework versions

Transformers 4.43.3
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.19.1

daisd-ai/anydef-orpo-v2

anydef-orpo-v2

Training procedure

Training hyperparameters

Training results

Framework versions

daisd-ai/
anydef-orpo-v2