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# Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor

This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8118
- Exact Match: 0.1555
- Rouge1: 0.5580
- Rouge2: 0.3886
- Rougel: 0.5407
- Rougelsum: 0.5483
- Bleu: 0.1297

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Exact Match | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu   |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:|
| 1.8525        | 0.16  | 500  | 0.9340          | 0.1435      | 0.5360 | 0.3596 | 0.5171 | 0.5238    | 0.1146 |
| 0.8679        | 0.32  | 1000 | 0.9262          | 0.1405      | 0.5385 | 0.3659 | 0.5228 | 0.5294    | 0.1179 |
| 0.8169        | 0.48  | 1500 | 0.8957          | 0.139       | 0.5372 | 0.3642 | 0.5192 | 0.5265    | 0.1135 |
| 0.7734        | 0.64  | 2000 | 0.8827          | 0.14        | 0.5485 | 0.3706 | 0.5316 | 0.5381    | 0.1210 |
| 0.743         | 0.8   | 2500 | 0.8647          | 0.155       | 0.5503 | 0.3833 | 0.5338 | 0.5411    | 0.1184 |
| 0.72          | 0.96  | 3000 | 0.8661          | 0.1545      | 0.5460 | 0.3735 | 0.5284 | 0.5366    | 0.1162 |
| 0.6539        | 1.12  | 3500 | 0.8591          | 0.156       | 0.5540 | 0.3841 | 0.5365 | 0.5444    | 0.1241 |
| 0.6301        | 1.28  | 4000 | 0.8452          | 0.1485      | 0.5556 | 0.3794 | 0.5369 | 0.5451    | 0.1237 |
| 0.6222        | 1.44  | 4500 | 0.8321          | 0.1585      | 0.5529 | 0.3818 | 0.5343 | 0.5430    | 0.1228 |
| 0.6221        | 1.6   | 5000 | 0.8317          | 0.154       | 0.5664 | 0.3925 | 0.5481 | 0.5575    | 0.1289 |
| 0.6067        | 1.76  | 5500 | 0.8228          | 0.1625      | 0.5607 | 0.3933 | 0.5438 | 0.5516    | 0.1299 |
| 0.5927        | 1.92  | 6000 | 0.8179          | 0.156       | 0.5625 | 0.3942 | 0.5457 | 0.5526    | 0.1309 |
| 0.5548        | 2.08  | 6500 | 0.8259          | 0.152       | 0.5582 | 0.3846 | 0.5402 | 0.5485    | 0.1314 |
| 0.5146        | 2.24  | 7000 | 0.8328          | 0.1545      | 0.5605 | 0.3889 | 0.5429 | 0.5514    | 0.1299 |
| 0.5193        | 2.4   | 7500 | 0.8197          | 0.1555      | 0.5604 | 0.3866 | 0.5431 | 0.5501    | 0.1268 |
| 0.5172        | 2.56  | 8000 | 0.8118          | 0.1555      | 0.5580 | 0.3886 | 0.5407 | 0.5483    | 0.1297 |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge", "bleu"], "model-index": [{"name": "Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor
===================================================


This model is a fine-tuned version of Salesforce/codet5-small on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8118
* Exact Match: 0.1555
* Rouge1: 0.5580
* Rouge2: 0.3886
* Rougel: 0.5407
* Rougelsum: 0.5483
* Bleu: 0.1297


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2