
<!-- 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. -->

# fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-large-with-ITTL-with-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: 0.8698
- Exact Match: 74.6073
- F1: 81.6214

## 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.2825        | 0.49  | 36   | 2.2341          | 49.2147     | 49.3071 |
| 3.465         | 0.98  | 72   | 1.8139          | 49.2147     | 49.4968 |
| 1.9165        | 1.48  | 108  | 1.3110          | 50.6545     | 59.1184 |
| 1.9165        | 1.97  | 144  | 0.9907          | 65.0524     | 72.4023 |
| 1.2487        | 2.46  | 180  | 0.9051          | 68.1937     | 75.7323 |
| 0.9426        | 2.95  | 216  | 0.8485          | 67.8010     | 75.3684 |
| 0.8069        | 3.45  | 252  | 0.8499          | 70.0262     | 77.7548 |
| 0.8069        | 3.94  | 288  | 0.9202          | 67.5393     | 74.8123 |
| 0.7193        | 4.44  | 324  | 0.7897          | 73.0366     | 79.9552 |
| 0.6234        | 4.92  | 360  | 0.7973          | 73.6911     | 80.5009 |
| 0.6234        | 5.42  | 396  | 0.8353          | 72.9058     | 80.2879 |
| 0.5583        | 5.91  | 432  | 0.8392          | 73.4293     | 80.6345 |
| 0.5263        | 6.41  | 468  | 0.8477          | 73.5602     | 81.0016 |
| 0.4642        | 6.9   | 504  | 0.8355          | 74.6073     | 81.7391 |
| 0.4642        | 7.39  | 540  | 0.8383          | 73.5602     | 81.1187 |
| 0.4381        | 7.88  | 576  | 0.8828          | 73.0366     | 79.8504 |
| 0.4099        | 8.38  | 612  | 0.8698          | 74.6073     | 81.6214 |


### 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-with-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-with-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: 0.8698
* Exact Match: 74.6073
* F1: 81.6214


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