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# fine-tuned-DatasetQAS-TYDI-QA-ID-with-xlm-roberta-large-without-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: 0.9538
- Exact Match: 69.0141
- F1: 82.7291

## 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.2063        | 0.5   | 19   | 3.6974          | 7.9225      | 18.1433 |
| 6.2063        | 0.99  | 38   | 2.5673          | 20.4225     | 30.5107 |
| 3.7106        | 1.5   | 57   | 1.5397          | 48.4155     | 64.0947 |
| 3.7106        | 1.99  | 76   | 1.2075          | 60.9155     | 74.9130 |
| 3.7106        | 2.5   | 95   | 1.0867          | 61.2676     | 75.6856 |
| 1.4112        | 2.99  | 114  | 0.9742          | 64.2606     | 78.6353 |
| 1.4112        | 3.5   | 133  | 0.9502          | 67.7817     | 81.5092 |
| 0.9522        | 3.99  | 152  | 0.9184          | 66.5493     | 80.7104 |
| 0.9522        | 4.5   | 171  | 0.9341          | 67.2535     | 81.5452 |
| 0.9522        | 4.99  | 190  | 0.9357          | 66.1972     | 81.2448 |
| 0.7334        | 5.5   | 209  | 0.9149          | 67.6056     | 81.7638 |
| 0.7334        | 5.99  | 228  | 0.9134          | 67.7817     | 82.2855 |
| 0.7334        | 6.5   | 247  | 0.9167          | 69.1901     | 82.3011 |
| 0.5938        | 6.99  | 266  | 0.9453          | 68.1338     | 82.0887 |
| 0.5938        | 7.5   | 285  | 0.9145          | 68.4859     | 82.8642 |
| 0.5273        | 7.99  | 304  | 0.9403          | 68.4859     | 82.5820 |
| 0.5273        | 8.5   | 323  | 0.9415          | 68.8380     | 82.4565 |
| 0.5273        | 8.99  | 342  | 0.9538          | 69.0141     | 82.7291 |


### 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-TYDI-QA-ID-with-xlm-roberta-large-without-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-TYDI-QA-ID-with-xlm-roberta-large-without-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: 0.9538
* Exact Match: 69.0141
* F1: 82.7291


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
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