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---
tags:
- generated_from_trainer
datasets:
- custom
model-index:
- name: xlm_r-joint_nlu-custom_ds
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_r-joint_nlu-custom_ds
This model was trained from scratch on the custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0312
- Intent Accuracy: 1.0
- Intent F1 Macro: 1.0
- Slot F1: 0.9506
- Semantic Accuracy: 0.9474
Evaluation on the test set:
- Intent Accuracy: 1.0
- Slot F1: 0.9506294471811714
- Semantic Accuracy: 0.9473684210526315
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Intent Accuracy | Intent F1 Macro | Slot F1 | Semantic Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:---------------:|:-------:|:-----------------:|
| No log | 1.0 | 47 | 2.1385 | 0.6809 | 0.4650 | 0.1429 | 0.1809 |
| No log | 2.0 | 94 | 1.0050 | 0.9043 | 0.8890 | 0.2806 | 0.2128 |
| No log | 3.0 | 141 | 0.4169 | 0.9787 | 0.9582 | 0.3632 | 0.2660 |
| No log | 4.0 | 188 | 0.2661 | 0.9894 | 0.9798 | 0.6908 | 0.5745 |
| No log | 5.0 | 235 | 0.2036 | 0.9894 | 0.9798 | 0.7454 | 0.5532 |
| No log | 6.0 | 282 | 0.1547 | 0.9894 | 0.9881 | 0.7699 | 0.6489 |
| No log | 7.0 | 329 | 0.1094 | 1.0 | 1.0 | 0.8216 | 0.6596 |
| No log | 8.0 | 376 | 0.1061 | 1.0 | 1.0 | 0.9080 | 0.7128 |
| No log | 9.0 | 423 | 0.0639 | 1.0 | 1.0 | 0.9575 | 0.8511 |
| No log | 10.0 | 470 | 0.0571 | 1.0 | 1.0 | 0.9597 | 0.8511 |
| 0.7099 | 11.0 | 517 | 0.0527 | 1.0 | 1.0 | 0.9763 | 0.8723 |
| 0.7099 | 12.0 | 564 | 0.0408 | 1.0 | 1.0 | 0.9708 | 0.8723 |
| 0.7099 | 13.0 | 611 | 0.0415 | 1.0 | 1.0 | 0.9899 | 0.9043 |
| 0.7099 | 14.0 | 658 | 0.0347 | 1.0 | 1.0 | 0.9661 | 0.9149 |
| 0.7099 | 15.0 | 705 | 0.0388 | 1.0 | 1.0 | 0.9899 | 0.9149 |
| 0.7099 | 16.0 | 752 | 0.0333 | 1.0 | 1.0 | 0.9983 | 0.9255 |
| 0.7099 | 17.0 | 799 | 0.0533 | 1.0 | 1.0 | 0.9899 | 0.8936 |
| 0.7099 | 18.0 | 846 | 0.0404 | 1.0 | 1.0 | 0.9899 | 0.9043 |
| 0.7099 | 19.0 | 893 | 0.0408 | 1.0 | 1.0 | 0.9805 | 0.9043 |
| 0.7099 | 20.0 | 940 | 0.0387 | 1.0 | 1.0 | 0.9899 | 0.9255 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0
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