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# mt5-base-finetuned-fa

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the pn_summary dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6477
- Rouge-1: 33.7
- Rouge-2: 21.28
- Rouge-l: 31.69
- Gen Len: 19.0
- Bertscore: 74.52

## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 5
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 3.3828        | 1.0   | 1875 | 2.8114          | 32.17   | 19.47   | 30.12   | 18.99   | 74.25     |
| 2.8204        | 2.0   | 3750 | 2.7080          | 32.67   | 19.92   | 30.56   | 19.0    | 74.31     |
| 2.6907        | 3.0   | 5625 | 2.6724          | 33.22   | 20.44   | 31.11   | 19.0    | 74.47     |
| 2.6029        | 4.0   | 7500 | 2.6513          | 33.46   | 20.75   | 31.38   | 19.0    | 74.54     |
| 2.5414        | 5.0   | 9375 | 2.6477          | 33.68   | 20.91   | 31.62   | 19.0    | 74.58     |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["summarization", "fa", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["pn_summary"], "model-index": [{"name": "mt5-base-finetuned-fa", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation summarization fa Abstractive Summarization generated_from_trainer dataset:pn_summary license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-base-finetuned-fa
=====================


This model is a fine-tuned version of google/mt5-base on the pn\_summary dataset.
It achieves the following results on the evaluation set:


* Loss: 2.6477
* Rouge-1: 33.7
* Rouge-2: 21.28
* Rouge-l: 31.69
* Gen Len: 19.0
* Bertscore: 74.52


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: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 5
* label\_smoothing\_factor: 0.1


### Training results



### Framework versions


* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 5
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 5
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #fa #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 5
* label\_smoothing\_factor: 0.1### Training results### Framework versions


* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1