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# bert-small-codesearchnet-python

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0582
- Bleu: 0.0347
- Rouge1: 0.6428
- Rouge2: 0.6252
- Avg Length: 17.891

## 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  | 1.2151          | 0.0    | 0.0928 | 0.0083 | 10.684     |
| 1.9359        | 2.0   | 750  | 1.0291          | 0.0032 | 0.1752 | 0.0338 | 15.0624    |
| 0.9422        | 3.0   | 1125 | 0.9173          | 0.0061 | 0.2506 | 0.0711 | 17.9358    |
| 0.776         | 4.0   | 1500 | 0.8058          | 0.0088 | 0.3321 | 0.1409 | 18.3724    |
| 0.776         | 5.0   | 1875 | 0.6915          | 0.0123 | 0.4044 | 0.2267 | 18.564     |
| 0.6218        | 6.0   | 2250 | 0.5281          | 0.0193 | 0.5382 | 0.4097 | 17.5586    |
| 0.4363        | 7.0   | 2625 | 0.1897          | 0.0333 | 0.6311 | 0.6002 | 17.8768    |
| 0.1518        | 8.0   | 3000 | 0.0834          | 0.0346 | 0.6413 | 0.621  | 17.879     |
| 0.1518        | 9.0   | 3375 | 0.0587          | 0.0349 | 0.6439 | 0.6268 | 17.8886    |
| 0.0579        | 10.0  | 3750 | 0.0547          | 0.0348 | 0.6443 | 0.6276 | 17.885     |
| 0.0437        | 11.0  | 4125 | 0.0525          | 0.0348 | 0.6442 | 0.6278 | 17.8766    |
| 0.0365        | 12.0  | 4500 | 0.0550          | 0.0347 | 0.6436 | 0.6266 | 17.8876    |
| 0.0365        | 13.0  | 4875 | 0.0545          | 0.0347 | 0.6439 | 0.627  | 17.876     |
| 0.032         | 14.0  | 5250 | 0.0539          | 0.0347 | 0.644  | 0.6268 | 17.8822    |
| 0.0288        | 15.0  | 5625 | 0.0582          | 0.0347 | 0.6428 | 0.6252 | 17.891     |


### Framework versions

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

{"tags": ["generated_from_trainer"], "metrics": ["bleu", "rouge"], "model-index": [{"name": "bert-small-codesearchnet-python", "results": []}]}
transformers pytorch tensorboard encoder-decoder text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

bert-small-codesearchnet-python
===============================


This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0582
* Bleu: 0.0347
* Rouge1: 0.6428
* Rouge2: 0.6252
* Avg Length: 17.891


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 #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #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