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---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-large-finetuned-augument-visquad2-27-3-2023-3
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. -->
# xlm-roberta-large-finetuned-augument-visquad2-27-3-2023-3
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:
- Best F1: 75.3631
- Loss: 2.0450
- Exact: 38.9165
- F1: 56.3720
- Total: 3821
- Hasans Exact: 55.9744
- Hasans F1: 81.1148
- Hasans Total: 2653
- Noans Exact: 0.1712
- Noans F1: 0.1712
- Noans Total: 1168
- Best Exact: 59.7749
- Best Exact Thresh: 0.5183
- Best F1 Thresh: 0.8690
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Best F1 | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Noans Exact | Noans F1 | Noans Total | Best Exact | Best Exact Thresh | Best F1 Thresh |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 0.8597 | 1.0 | 4221 | 66.4890 | 1.2255 | 36.1947 | 54.1414 | 3821 | 52.1297 | 77.9775 | 2653 | 0.0 | 0.0 | 1168 | 52.9704 | 0.8158 | 0.9074 |
| 0.4623 | 2.0 | 8443 | 70.0050 | 1.1813 | 37.8173 | 55.5970 | 3821 | 54.4666 | 80.0740 | 2653 | 0.0 | 0.0 | 1168 | 55.1950 | 0.7529 | 0.8275 |
| 0.2999 | 3.0 | 12664 | 75.0810 | 1.2417 | 39.8587 | 56.3329 | 3821 | 57.3690 | 81.0961 | 2653 | 0.0856 | 0.0856 | 1168 | 60.4030 | 0.9294 | 0.9459 |
| 0.1915 | 4.0 | 16886 | 74.7037 | 1.6500 | 38.7333 | 56.2476 | 3821 | 55.7482 | 80.9733 | 2653 | 0.0856 | 0.0856 | 1168 | 58.6496 | 0.7690 | 0.9767 |
| 0.1185 | 5.0 | 21105 | 75.3631 | 2.0450 | 38.9165 | 56.3720 | 3821 | 55.9744 | 81.1148 | 2653 | 0.1712 | 0.1712 | 1168 | 59.7749 | 0.5183 | 0.8690 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
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