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# t5-base-finetuned-resumes_t2json_large-peft

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the resumes_t2json_large dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 0.0

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0           | 1.0   | 20560 | nan             | 0.0    | 0.0    | 0.0    | 0.0       | 0.0     |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["resumes_t2json_large"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-finetuned-resumes_t2json_large-peft", "results": []}]}
pytorch tensorboard generated_from_trainer dataset:resumes_t2json_large license:apache-2.0 region:us
TAGS
#pytorch #tensorboard #generated_from_trainer #dataset-resumes_t2json_large #license-apache-2.0 #region-us 

t5-base-finetuned-resumes\_t2json\_large-peft
=============================================


This model is a fine-tuned version of t5-base on the resumes\_t2json\_large dataset.
It achieves the following results on the evaluation set:


* Loss: nan
* Rouge1: 0.0
* Rouge2: 0.0
* Rougel: 0.0
* Rougelsum: 0.0
* Gen Len: 0.0


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#pytorch #tensorboard #generated_from_trainer #dataset-resumes_t2json_large #license-apache-2.0 #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#pytorch #tensorboard #generated_from_trainer #dataset-resumes_t2json_large #license-apache-2.0 #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2