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# fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8090
- Exact Match: 74.0838
- F1: 80.8517

## 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: 1e-05
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.1945        | 0.49  | 36   | 2.3430          | 50.0        | 50.0    |
| 3.5404        | 0.98  | 72   | 1.8265          | 48.5602     | 52.5772 |
| 1.9365        | 1.48  | 108  | 1.2750          | 59.4241     | 67.2505 |
| 1.9365        | 1.97  | 144  | 1.0492          | 66.0995     | 73.9501 |
| 1.2265        | 2.46  | 180  | 0.9042          | 69.7644     | 77.5779 |
| 0.9482        | 2.95  | 216  | 0.8393          | 71.2042     | 78.9342 |
| 0.7866        | 3.45  | 252  | 0.8805          | 70.8115     | 78.2310 |
| 0.7866        | 3.94  | 288  | 0.9333          | 69.5026     | 76.5121 |
| 0.6871        | 4.44  | 324  | 0.8045          | 75.3927     | 82.4815 |
| 0.6086        | 4.92  | 360  | 0.7908          | 75.5236     | 81.8859 |
| 0.6086        | 5.42  | 396  | 0.8351          | 73.0366     | 80.3624 |
| 0.5449        | 5.91  | 432  | 0.8090          | 74.0838     | 80.8517 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 

fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-large-with-ITTL-without-freeze-LR-1e-05
======================================================================================


This model is a fine-tuned version of xlm-roberta-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8090
* Exact Match: 74.0838
* F1: 80.8517


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: 1e-05
* 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: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* 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: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* 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: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2