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# Salesforce-codet5-small-CodeXGLUE-CONCODE-test

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: 0.8508
- Exact Match: 0.156
- Rouge1: 0.5559
- Rouge2: 0.3857
- Rougel: 0.5378
- Rougelsum: 0.5465
- Bleu: 0.1246

## 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: 0.001
- 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: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Exact Match | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu   |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:|
| 1.3563        | 0.16  | 500  | 1.1652          | 0.1115      | 0.5098 | 0.3191 | 0.4915 | 0.4982    | 0.1088 |
| 0.9656        | 0.32  | 1000 | 1.0435          | 0.1245      | 0.5246 | 0.3444 | 0.5075 | 0.5145    | 0.1164 |
| 0.8627        | 0.48  | 1500 | 0.9851          | 0.121       | 0.5275 | 0.3420 | 0.5074 | 0.5154    | 0.1132 |
| 0.7718        | 0.64  | 2000 | 0.9288          | 0.1385      | 0.5334 | 0.3589 | 0.5174 | 0.5242    | 0.1206 |
| 0.7237        | 0.8   | 2500 | 0.8867          | 0.1495      | 0.5505 | 0.3762 | 0.5328 | 0.5406    | 0.1208 |
| 0.6812        | 0.96  | 3000 | 0.8508          | 0.156       | 0.5559 | 0.3857 | 0.5378 | 0.5465    | 0.1246 |


### 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-test", "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-test
==============================================


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: 0.8508
* Exact Match: 0.156
* Rouge1: 0.5559
* Rouge2: 0.3857
* Rougel: 0.5378
* Rougelsum: 0.5465
* Bleu: 0.1246


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: 0.001
* 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: 1
* 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: 0.001
* 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: 1
* 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: 0.001
* 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: 1
* 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: 0.001
* 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: 1
* 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