transformers tensorboard safetensors t5 text2text-generation generated_from_trainer base_model:Salesforce/codet5-small license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: Salesforce/codet5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: codet5-small-ft-v10.1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# codet5-small-ft-v10.1

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: 0.2959
- Rouge1: 81.3062
- Rouge2: 75.041
- Rougel: 81.6284
- Rougelsum: 81.0728
- Gen Len: 13.9857

## Model description

More information needed
### FORMAT 
[BUG] reader = ReaderFactory.newXmlReader( null) ; [CONTEXT] outputFile = output.getFile(); } Reader reader; try { if (inputEncoding != null) { if (parser.getType() == Parser.XML_TYPE) { <extra_id_0> } else { reader = ReaderFactory.newReader(inputFile, inputEncoding); } } else { reader = ReaderFactory.newPlatformReader(inputFile); }

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 19   | 1.8175          | 54.4184 | 41.9095 | 54.2713 | 53.6157   | 12.8857 |
| No log        | 2.0   | 38   | 0.8332          | 69.024  | 55.6554 | 66.6238 | 66.2558   | 13.2286 |
| No log        | 3.0   | 57   | 0.5293          | 64.9854 | 48.4359 | 62.4491 | 61.9973   | 12.8857 |
| No log        | 4.0   | 76   | 0.4513          | 70.6432 | 59.744  | 69.8409 | 69.4408   | 14.3    |
| No log        | 5.0   | 95   | 0.3845          | 73.7268 | 64.6645 | 73.4703 | 72.9311   | 14.3571 |
| No log        | 6.0   | 114  | 0.3549          | 75.2727 | 66.4769 | 75.1021 | 74.6361   | 14.4571 |
| No log        | 7.0   | 133  | 0.3199          | 78.5225 | 72.2594 | 78.8828 | 78.3729   | 13.9    |
| No log        | 8.0   | 152  | 0.3053          | 79.2229 | 72.7468 | 79.6079 | 79.1507   | 14.2429 |
| No log        | 9.0   | 171  | 0.2998          | 80.4535 | 73.8457 | 80.587  | 80.1368   | 14.1571 |
| No log        | 10.0  | 190  | 0.2959          | 81.3062 | 75.041  | 81.6284 | 81.0728   | 13.9857 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
