| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: relevant_profession |
| 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. --> |
|
|
| # relevant_profession |
| |
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.8640 |
| - Acc At K: 0.9666 |
| - Acc: 0.7152 |
| |
| ## 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: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 8 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Acc At K | Acc | |
| |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| |
| | 3.6523 | 0.5 | 18304 | 2.1025 | 0.8154 | 0.5818 | |
| | 1.7365 | 1.0 | 36608 | 1.4014 | 0.9097 | 0.6590 | |
| | 1.2636 | 1.5 | 54912 | 1.1711 | 0.9350 | 0.6845 | |
| | 1.092 | 2.0 | 73216 | 1.0605 | 0.9473 | 0.6931 | |
| | 0.9662 | 2.5 | 91520 | 1.0046 | 0.9533 | 0.7012 | |
| | 0.9233 | 3.0 | 109824 | 0.9643 | 0.9573 | 0.7025 | |
| | 0.8521 | 3.5 | 128128 | 0.9436 | 0.9594 | 0.7060 | |
| | 0.8334 | 4.0 | 146432 | 0.9189 | 0.9616 | 0.7078 | |
| | 0.7845 | 4.5 | 164736 | 0.9082 | 0.9631 | 0.7091 | |
| | 0.7754 | 5.0 | 183040 | 0.8953 | 0.9639 | 0.7105 | |
| | 0.7355 | 5.5 | 201344 | 0.8907 | 0.9646 | 0.7108 | |
| | 0.7334 | 6.0 | 219648 | 0.8795 | 0.9649 | 0.7124 | |
| | 0.6991 | 6.5 | 237952 | 0.8772 | 0.9657 | 0.7132 | |
| | 0.7001 | 7.0 | 256256 | 0.8670 | 0.9662 | 0.7129 | |
| | 0.674 | 7.5 | 274560 | 0.8667 | 0.9664 | 0.7149 | |
| | 0.672 | 8.0 | 292864 | 0.8640 | 0.9666 | 0.7152 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.26.1 |
| - Pytorch 2.0.0+cu117 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
| |
