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Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
Inference Endpoints
text-generation-inference
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - rouge
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+ - bleu
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+ model-index:
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+ - name: Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor
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+
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+ This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8118
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+ - Exact Match: 0.1555
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+ - Rouge1: 0.5580
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+ - Rouge2: 0.3886
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+ - Rougel: 0.5407
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+ - Rougelsum: 0.5483
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+ - Bleu: 0.1297
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.05
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+ - num_epochs: 10
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Exact Match | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:|
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+ | 1.8525 | 0.16 | 500 | 0.9340 | 0.1435 | 0.5360 | 0.3596 | 0.5171 | 0.5238 | 0.1146 |
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+ | 0.8679 | 0.32 | 1000 | 0.9262 | 0.1405 | 0.5385 | 0.3659 | 0.5228 | 0.5294 | 0.1179 |
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+ | 0.8169 | 0.48 | 1500 | 0.8957 | 0.139 | 0.5372 | 0.3642 | 0.5192 | 0.5265 | 0.1135 |
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+ | 0.7734 | 0.64 | 2000 | 0.8827 | 0.14 | 0.5485 | 0.3706 | 0.5316 | 0.5381 | 0.1210 |
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+ | 0.743 | 0.8 | 2500 | 0.8647 | 0.155 | 0.5503 | 0.3833 | 0.5338 | 0.5411 | 0.1184 |
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+ | 0.72 | 0.96 | 3000 | 0.8661 | 0.1545 | 0.5460 | 0.3735 | 0.5284 | 0.5366 | 0.1162 |
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+ | 0.6539 | 1.12 | 3500 | 0.8591 | 0.156 | 0.5540 | 0.3841 | 0.5365 | 0.5444 | 0.1241 |
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+ | 0.6301 | 1.28 | 4000 | 0.8452 | 0.1485 | 0.5556 | 0.3794 | 0.5369 | 0.5451 | 0.1237 |
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+ | 0.6222 | 1.44 | 4500 | 0.8321 | 0.1585 | 0.5529 | 0.3818 | 0.5343 | 0.5430 | 0.1228 |
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+ | 0.6221 | 1.6 | 5000 | 0.8317 | 0.154 | 0.5664 | 0.3925 | 0.5481 | 0.5575 | 0.1289 |
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+ | 0.6067 | 1.76 | 5500 | 0.8228 | 0.1625 | 0.5607 | 0.3933 | 0.5438 | 0.5516 | 0.1299 |
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+ | 0.5927 | 1.92 | 6000 | 0.8179 | 0.156 | 0.5625 | 0.3942 | 0.5457 | 0.5526 | 0.1309 |
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+ | 0.5548 | 2.08 | 6500 | 0.8259 | 0.152 | 0.5582 | 0.3846 | 0.5402 | 0.5485 | 0.1314 |
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+ | 0.5146 | 2.24 | 7000 | 0.8328 | 0.1545 | 0.5605 | 0.3889 | 0.5429 | 0.5514 | 0.1299 |
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+ | 0.5193 | 2.4 | 7500 | 0.8197 | 0.1555 | 0.5604 | 0.3866 | 0.5431 | 0.5501 | 0.1268 |
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+ | 0.5172 | 2.56 | 8000 | 0.8118 | 0.1555 | 0.5580 | 0.3886 | 0.5407 | 0.5483 | 0.1297 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.27.1
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.10.1
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+ - Tokenizers 0.13.2