
<!-- 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-indobert-large-p2-with-ITTL-with-freeze-LR-1e-05

This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2708
- Exact Match: 52.7487
- F1: 60.8071

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.4745        | 0.49  | 36   | 2.5724          | 35.6021     | 37.8405 |
| 3.5197        | 0.98  | 72   | 1.9912          | 28.0105     | 35.4278 |
| 2.1756        | 1.48  | 108  | 1.6669          | 35.7330     | 43.0612 |
| 2.1756        | 1.97  | 144  | 1.5047          | 39.3979     | 46.1664 |
| 1.6725        | 2.46  | 180  | 1.3222          | 45.9424     | 52.9355 |
| 1.336         | 2.95  | 216  | 1.3205          | 44.1099     | 51.6851 |
| 1.176         | 3.45  | 252  | 1.2526          | 47.5131     | 55.3298 |
| 1.176         | 3.94  | 288  | 1.2778          | 47.3822     | 54.7110 |
| 1.1089        | 4.44  | 324  | 1.2291          | 49.8691     | 57.2303 |
| 0.967         | 4.93  | 360  | 1.1944          | 52.4869     | 60.2202 |
| 0.967         | 5.42  | 396  | 1.2122          | 53.7958     | 61.3033 |
| 0.9202        | 5.91  | 432  | 1.2348          | 54.0576     | 61.6263 |
| 0.8719        | 6.41  | 468  | 1.2206          | 55.2356     | 62.9267 |
| 0.8205        | 6.9   | 504  | 1.2472          | 53.9267     | 61.6359 |
| 0.8205        | 7.4   | 540  | 1.2764          | 52.3560     | 60.2681 |
| 0.7907        | 7.89  | 576  | 1.2382          | 55.3665     | 63.0145 |
| 0.7533        | 8.38  | 612  | 1.2812          | 52.4869     | 60.4214 |
| 0.7533        | 8.87  | 648  | 1.2474          | 53.1414     | 60.6338 |
| 0.7345        | 9.37  | 684  | 1.2708          | 52.7487     | 60.8071 |


### 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-indobert-large-p2-with-ITTL-with-freeze-LR-1e-05", "results": []}]}
transformers pytorch tensorboard bert question-answering generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 

fine-tuned-DatasetQAS-IDK-MRC-with-indobert-large-p2-with-ITTL-with-freeze-LR-1e-05
===================================================================================


This model is a fine-tuned version of indobenchmark/indobert-large-p2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2708
* Exact Match: 52.7487
* F1: 60.8071


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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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 #bert #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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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