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# t5-small-codesearchnet-multilang-python

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0682
- Bleu: 0.0401
- Rouge1: 0.6333
- Rouge2: 0.6147
- Avg Length: 16.9514

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Rouge1 | Rouge2 | Avg Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:----------:|
| No log        | 1.0   | 375  | 0.0717          | 0.0407 | 0.6244 | 0.6063 | 17.046     |
| 1.6287        | 2.0   | 750  | 0.0589          | 0.041  | 0.6321 | 0.6136 | 16.9924    |
| 0.0592        | 3.0   | 1125 | 0.0551          | 0.0402 | 0.6334 | 0.6152 | 16.971     |
| 0.0511        | 4.0   | 1500 | 0.0542          | 0.0402 | 0.6336 | 0.6155 | 16.9718    |
| 0.0511        | 5.0   | 1875 | 0.0529          | 0.0401 | 0.6343 | 0.6161 | 16.961     |
| 0.0441        | 6.0   | 2250 | 0.0531          | 0.0402 | 0.6341 | 0.6158 | 16.9626    |
| 0.0399        | 7.0   | 2625 | 0.0521          | 0.0402 | 0.6337 | 0.6154 | 16.97      |
| 0.0351        | 8.0   | 3000 | 0.0547          | 0.0401 | 0.6341 | 0.6159 | 16.964     |
| 0.0351        | 9.0   | 3375 | 0.0545          | 0.0402 | 0.635  | 0.6167 | 16.962     |
| 0.0301        | 10.0  | 3750 | 0.0557          | 0.0402 | 0.6342 | 0.6159 | 16.9646    |
| 0.027         | 11.0  | 4125 | 0.0569          | 0.0402 | 0.6342 | 0.6157 | 16.9622    |
| 0.0239        | 12.0  | 4500 | 0.0606          | 0.0401 | 0.6342 | 0.6158 | 16.9564    |
| 0.0239        | 13.0  | 4875 | 0.0616          | 0.0401 | 0.6343 | 0.6163 | 16.963     |
| 0.02          | 14.0  | 5250 | 0.0672          | 0.0401 | 0.6336 | 0.6154 | 16.9648    |
| 0.0185        | 15.0  | 5625 | 0.0682          | 0.0401 | 0.6333 | 0.6147 | 16.9514    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu", "rouge"], "model-index": [{"name": "t5-small-codesearchnet-multilang-python", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-codesearchnet-multilang-python
=======================================


This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0682
* Bleu: 0.0401
* Rouge1: 0.6333
* Rouge2: 0.6147
* Avg Length: 16.9514


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 10
* total\_train\_batch\_size: 80
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 10
* total\_train\_batch\_size: 80
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 10
* total\_train\_batch\_size: 80
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3