---
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: []
---

<!-- 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: 0.8236
- Exact Match: 75.9162
- F1: 81.7215

## 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.4323        | 0.49  | 36   | 2.3534          | 50.0        | 50.0    |
| 3.6036        | 0.98  | 72   | 1.7984          | 47.5131     | 48.0031 |
| 1.8711        | 1.48  | 108  | 1.1297          | 59.1623     | 67.4774 |
| 1.8711        | 1.97  | 144  | 1.0215          | 62.9581     | 70.7402 |
| 1.1663        | 2.46  | 180  | 0.8500          | 69.1099     | 76.2124 |
| 0.9042        | 2.95  | 216  | 0.8389          | 68.8482     | 76.0043 |
| 0.7502        | 3.45  | 252  | 0.8404          | 70.9424     | 78.2197 |
| 0.7502        | 3.94  | 288  | 0.9341          | 68.5864     | 75.3916 |
| 0.6715        | 4.44  | 324  | 0.7647          | 74.2147     | 80.2681 |
| 0.576         | 4.92  | 360  | 0.7881          | 75.6545     | 81.9120 |
| 0.576         | 5.42  | 396  | 0.8022          | 74.7382     | 80.7782 |
| 0.5213        | 5.91  | 432  | 0.8218          | 74.2147     | 80.6803 |
| 0.4811        | 6.41  | 468  | 0.8236          | 75.9162     | 81.7215 |


### Framework versions

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