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# CodeBERTa-commit-message-autocomplete

This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8906
- Accuracy: 0.6346

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 40   | 4.5523          | 0.3432   |
| No log        | 2.0   | 80   | 3.8711          | 0.3796   |
| No log        | 3.0   | 120  | 3.2419          | 0.4503   |
| No log        | 4.0   | 160  | 2.8709          | 0.4962   |
| No log        | 5.0   | 200  | 2.6999          | 0.5085   |
| No log        | 6.0   | 240  | 2.6622          | 0.5216   |
| No log        | 7.0   | 280  | 2.5048          | 0.5410   |
| No log        | 8.0   | 320  | 2.4249          | 0.5581   |
| No log        | 9.0   | 360  | 2.3727          | 0.5623   |
| No log        | 10.0  | 400  | 2.3625          | 0.5665   |
| No log        | 11.0  | 440  | 2.3320          | 0.5706   |
| No log        | 12.0  | 480  | 2.1704          | 0.5950   |
| 3.081         | 13.0  | 520  | 2.2109          | 0.5893   |
| 3.081         | 14.0  | 560  | 2.2330          | 0.5884   |
| 3.081         | 15.0  | 600  | 2.1454          | 0.5954   |
| 3.081         | 16.0  | 640  | 2.1740          | 0.5951   |
| 3.081         | 17.0  | 680  | 2.1219          | 0.5920   |
| 3.081         | 18.0  | 720  | 2.1136          | 0.6052   |
| 3.081         | 19.0  | 760  | 2.0586          | 0.6127   |
| 3.081         | 20.0  | 800  | 2.0185          | 0.6113   |
| 3.081         | 21.0  | 840  | 2.0493          | 0.6129   |
| 3.081         | 22.0  | 880  | 1.9766          | 0.6217   |
| 3.081         | 23.0  | 920  | 1.9968          | 0.6189   |
| 3.081         | 24.0  | 960  | 1.9567          | 0.6276   |
| 2.122         | 25.0  | 1000 | 1.9611          | 0.6269   |
| 2.122         | 26.0  | 1040 | 1.9437          | 0.6254   |
| 2.122         | 27.0  | 1080 | 1.9865          | 0.6266   |
| 2.122         | 28.0  | 1120 | 1.9112          | 0.6295   |
| 2.122         | 29.0  | 1160 | 1.8903          | 0.6292   |
| 2.122         | 30.0  | 1200 | 1.8992          | 0.6376   |
| 2.122         | 31.0  | 1240 | 1.9122          | 0.6327   |
| 2.122         | 32.0  | 1280 | 1.8906          | 0.6346   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "CodeBERTa-commit-message-autocomplete", "results": []}]}
transformers pytorch roberta fill-mask generated_from_trainer autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us 

CodeBERTa-commit-message-autocomplete
=====================================


This model is a fine-tuned version of microsoft/codebert-base-mlm on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8906
* Accuracy: 0.6346


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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 1024
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 1024
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 1024
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 1024
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2