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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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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-cl-cardiff_cl_only_delta |
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results: [] |
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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-cl-cardiff_cl_only_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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0448 |
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- Accuracy: 0.4838 |
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- F1: 0.4798 |
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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: 30 |
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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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| No log | 1.09 | 250 | 1.0988 | 0.3333 | 0.1667 | |
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| 1.0959 | 2.17 | 500 | 1.0989 | 0.3333 | 0.1667 | |
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| 1.0959 | 3.26 | 750 | 1.1000 | 0.3333 | 0.1667 | |
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| 1.0996 | 4.35 | 1000 | 1.1023 | 0.3333 | 0.1667 | |
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| 1.0996 | 5.43 | 1250 | 1.0990 | 0.3333 | 0.1667 | |
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| 1.1001 | 6.52 | 1500 | 1.0997 | 0.3333 | 0.1667 | |
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| 1.1001 | 7.61 | 1750 | 1.0998 | 0.3333 | 0.1667 | |
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| 1.0992 | 8.7 | 2000 | 1.0988 | 0.3333 | 0.1667 | |
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| 1.0992 | 9.78 | 2250 | 1.0990 | 0.3333 | 0.1667 | |
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| 1.0998 | 10.87 | 2500 | 1.0992 | 0.3333 | 0.1667 | |
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| 1.0998 | 11.96 | 2750 | 1.0996 | 0.3333 | 0.1667 | |
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| 1.0994 | 13.04 | 3000 | 1.0987 | 0.3333 | 0.1667 | |
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| 1.0994 | 14.13 | 3250 | 1.0988 | 0.3333 | 0.1667 | |
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| 1.0993 | 15.22 | 3500 | 1.0993 | 0.3333 | 0.1667 | |
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| 1.0993 | 16.3 | 3750 | 1.0987 | 0.3333 | 0.1667 | |
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| 1.0995 | 17.39 | 4000 | 1.0986 | 0.3333 | 0.1667 | |
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| 1.0995 | 18.48 | 4250 | 1.0989 | 0.3333 | 0.1667 | |
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| 1.0991 | 19.57 | 4500 | 1.0989 | 0.3333 | 0.1667 | |
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| 1.0991 | 20.65 | 4750 | 1.0987 | 0.3333 | 0.1667 | |
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| 1.0994 | 21.74 | 5000 | 1.0987 | 0.3333 | 0.1667 | |
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| 1.0994 | 22.83 | 5250 | 1.0987 | 0.3333 | 0.1667 | |
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| 1.0991 | 23.91 | 5500 | 1.0987 | 0.3333 | 0.1667 | |
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| 1.0991 | 25.0 | 5750 | 1.0986 | 0.3333 | 0.1667 | |
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| 1.0991 | 26.09 | 6000 | 1.0987 | 0.3333 | 0.1667 | |
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| 1.0991 | 27.17 | 6250 | 1.0986 | 0.3333 | 0.1667 | |
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| 1.0946 | 28.26 | 6500 | 1.0796 | 0.4560 | 0.4220 | |
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| 1.0946 | 29.35 | 6750 | 1.0448 | 0.4838 | 0.4798 | |
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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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