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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: job-listing-relevance-model |
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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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# job-listing-relevance-model |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0555 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.6791 | 0.43 | 50 | 0.4885 | |
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| 0.2491 | 0.87 | 100 | 0.1554 | |
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| 0.1067 | 1.3 | 150 | 0.1085 | |
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| 0.1382 | 1.74 | 200 | 0.1509 | |
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| 0.0575 | 2.17 | 250 | 0.1194 | |
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| 0.0386 | 2.61 | 300 | 0.0973 | |
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| 0.1782 | 3.04 | 350 | 0.1145 | |
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| 0.0458 | 3.48 | 400 | 0.0852 | |
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| 0.0043 | 3.91 | 450 | 0.0878 | |
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| 0.0374 | 4.35 | 500 | 0.0673 | |
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| 0.09 | 4.78 | 550 | 0.0483 | |
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| 0.0017 | 5.22 | 600 | 0.0978 | |
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| 0.0025 | 5.65 | 650 | 0.0792 | |
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| 0.0138 | 6.09 | 700 | 0.0358 | |
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| 0.0716 | 6.52 | 750 | 0.0463 | |
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| 0.0391 | 6.95 | 800 | 0.1162 | |
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| 0.0164 | 7.39 | 850 | 0.0509 | |
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| 0.0359 | 7.82 | 900 | 0.0757 | |
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| 0.0012 | 8.26 | 950 | 0.0337 | |
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| 0.0008 | 8.69 | 1000 | 0.0585 | |
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| 0.0007 | 9.13 | 1050 | 0.0655 | |
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| 0.0609 | 9.56 | 1100 | 0.0481 | |
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| 0.0007 | 10.0 | 1150 | 0.0555 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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