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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- generated_from_trainer |
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- nlu |
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- intent-classification |
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- text-classification |
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datasets: |
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- AmazonScience/massive |
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metrics: |
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- accuracy |
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- f1 |
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base_model: xlm-roberta-base |
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model-index: |
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- name: xlm-r-base-amazon-massive-intent-label_smoothing |
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results: |
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- task: |
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type: intent-classification |
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name: intent-classification |
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dataset: |
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name: MASSIVE |
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type: AmazonScience/massive |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.8879 |
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name: F1 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlm-r-base-amazon-massive-intent-label_smoothing |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5148 |
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- Accuracy: 0.8879 |
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- F1: 0.8879 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- label_smoothing_factor: 0.4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 3.3945 | 1.0 | 720 | 2.7175 | 0.7900 | 0.7900 | |
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| 2.7629 | 2.0 | 1440 | 2.5660 | 0.8549 | 0.8549 | |
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| 2.5143 | 3.0 | 2160 | 2.5389 | 0.8711 | 0.8711 | |
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| 2.4678 | 4.0 | 2880 | 2.5172 | 0.8883 | 0.8883 | |
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| 2.4187 | 5.0 | 3600 | 2.5148 | 0.8879 | 0.8879 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.0 |
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- Tokenizers 0.13.2 |
