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--- |
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license: mit |
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base_model: facebook/xlm-v-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- massive |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: scenario-TCR-XLMV_data-AmazonScience_massive_all_1_1_delta |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: massive |
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type: massive |
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config: all_1.1 |
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split: validation |
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args: all_1.1 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.051647811116576486 |
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- name: F1 |
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type: f1 |
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value: 0.0016647904742274576 |
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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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# scenario-TCR-XLMV_data-AmazonScience_massive_all_1_1_delta |
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This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.8343 |
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- Accuracy: 0.0516 |
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- F1: 0.0017 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 11213 |
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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: 500 |
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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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| 3.7591 | 0.27 | 5000 | 3.7340 | 0.0644 | 0.0021 | |
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| 3.7519 | 0.53 | 10000 | 3.7681 | 0.0620 | 0.0020 | |
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| 3.7417 | 0.8 | 15000 | 3.8022 | 0.0620 | 0.0020 | |
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| 3.7237 | 1.07 | 20000 | 3.8134 | 0.0516 | 0.0017 | |
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| 3.7217 | 1.34 | 25000 | 3.8412 | 0.0516 | 0.0017 | |
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| 3.7172 | 1.6 | 30000 | 3.8343 | 0.0516 | 0.0017 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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