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# greek-m2m100-5ep-384

This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2996

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.903         | 0.67  | 100  | 1.4264          |
| 1.3766        | 1.35  | 200  | 1.3557          |
| 1.2595        | 2.03  | 300  | 1.3305          |
| 1.1093        | 2.7   | 400  | 1.3136          |
| 1.0445        | 3.38  | 500  | 1.3070          |
| 0.9852        | 4.05  | 600  | 1.3037          |
| 0.9114        | 4.73  | 700  | 1.2996          |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "greek-m2m100-5ep-384", "results": []}]}
transformers pytorch m2m_100 text2text-generation generated_from_trainer dataset:squad license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #m2m_100 #text2text-generation #generated_from_trainer #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #region-us 

greek-m2m100-5ep-384
====================


This model is a fine-tuned version of facebook/m2m100\_418M on the squad dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2996


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #m2m_100 #text2text-generation #generated_from_trainer #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #m2m_100 #text2text-generation #generated_from_trainer #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2