lmeninato/bert-small-codesearchnet-python

1---2tags:3- generated_from_trainer4metrics:5- bleu6- rouge7model-index:8- name: bert-small-codesearchnet-python9  results: []10---11 12<!-- This model card has been generated automatically according to the information the Trainer had access to. You13should probably proofread and complete it, then remove this comment. -->14 15# bert-small-codesearchnet-python16 17This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.18It achieves the following results on the evaluation set:19- Loss: 0.058220- Bleu: 0.034721- Rouge1: 0.642822- Rouge2: 0.625223- Avg Length: 17.89124 25## Model description26 27More information needed28 29## Intended uses & limitations30 31More information needed32 33## Training and evaluation data34 35More information needed36 37## Training procedure38 39### Training hyperparameters40 41The following hyperparameters were used during training:42- learning_rate: 5e-0543- train_batch_size: 844- eval_batch_size: 845- seed: 4246- gradient_accumulation_steps: 1047- total_train_batch_size: 8048- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0849- lr_scheduler_type: linear50- num_epochs: 1551 52### Training results53 54| Training Loss | Epoch | Step | Validation Loss | Bleu   | Rouge1 | Rouge2 | Avg Length |55|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:----------:|56| No log        | 1.0   | 375  | 1.2151          | 0.0    | 0.0928 | 0.0083 | 10.684     |57| 1.9359        | 2.0   | 750  | 1.0291          | 0.0032 | 0.1752 | 0.0338 | 15.0624    |58| 0.9422        | 3.0   | 1125 | 0.9173          | 0.0061 | 0.2506 | 0.0711 | 17.9358    |59| 0.776         | 4.0   | 1500 | 0.8058          | 0.0088 | 0.3321 | 0.1409 | 18.3724    |60| 0.776         | 5.0   | 1875 | 0.6915          | 0.0123 | 0.4044 | 0.2267 | 18.564     |61| 0.6218        | 6.0   | 2250 | 0.5281          | 0.0193 | 0.5382 | 0.4097 | 17.5586    |62| 0.4363        | 7.0   | 2625 | 0.1897          | 0.0333 | 0.6311 | 0.6002 | 17.8768    |63| 0.1518        | 8.0   | 3000 | 0.0834          | 0.0346 | 0.6413 | 0.621  | 17.879     |64| 0.1518        | 9.0   | 3375 | 0.0587          | 0.0349 | 0.6439 | 0.6268 | 17.8886    |65| 0.0579        | 10.0  | 3750 | 0.0547          | 0.0348 | 0.6443 | 0.6276 | 17.885     |66| 0.0437        | 11.0  | 4125 | 0.0525          | 0.0348 | 0.6442 | 0.6278 | 17.8766    |67| 0.0365        | 12.0  | 4500 | 0.0550          | 0.0347 | 0.6436 | 0.6266 | 17.8876    |68| 0.0365        | 13.0  | 4875 | 0.0545          | 0.0347 | 0.6439 | 0.627  | 17.876     |69| 0.032         | 14.0  | 5250 | 0.0539          | 0.0347 | 0.644  | 0.6268 | 17.8822    |70| 0.0288        | 15.0  | 5625 | 0.0582          | 0.0347 | 0.6428 | 0.6252 | 17.891     |71 72 73### Framework versions74 75- Transformers 4.28.176- Pytorch 2.0.0+cu11877- Datasets 2.12.078- Tokenizers 0.13.379