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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
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