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---
library_name: transformers
base_model: MHGanainy/xmod-shared-roberta-base-legal-multi
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xmod-shared-roberta-base-legal-multi-downstream-build_rr
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. -->
# xmod-shared-roberta-base-legal-multi-downstream-build_rr
This model is a fine-tuned version of [MHGanainy/xmod-shared-roberta-base-legal-multi](https://huggingface.co/MHGanainy/xmod-shared-roberta-base-legal-multi) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9259
- Precision-macro: 0.6337
- Recall-macro: 0.5884
- Macro-f1: 0.6021
- Precision-micro: 0.7895
- Recall-micro: 0.7895
- Micro-f1: 0.7895
- Accuracy: 0.7895
## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision-macro | Recall-macro | Macro-f1 | Precision-micro | Recall-micro | Micro-f1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:--------:|
| No log | 1.0 | 124 | 0.8663 | 0.5370 | 0.4683 | 0.4780 | 0.7461 | 0.7461 | 0.7461 | 0.7461 |
| No log | 2.0 | 248 | 0.9013 | 0.4938 | 0.5409 | 0.5023 | 0.6982 | 0.6982 | 0.6982 | 0.6982 |
| No log | 3.0 | 372 | 0.7797 | 0.5765 | 0.5451 | 0.5415 | 0.7635 | 0.7635 | 0.7635 | 0.7635 |
| No log | 4.0 | 496 | 0.7203 | 0.6530 | 0.5478 | 0.5409 | 0.7718 | 0.7718 | 0.7718 | 0.7718 |
| 0.9675 | 5.0 | 620 | 0.7465 | 0.5984 | 0.5960 | 0.5866 | 0.7777 | 0.7777 | 0.7777 | 0.7777 |
| 0.9675 | 6.0 | 744 | 0.7503 | 0.6134 | 0.5692 | 0.5699 | 0.7791 | 0.7791 | 0.7791 | 0.7791 |
| 0.9675 | 7.0 | 868 | 0.7665 | 0.6552 | 0.5732 | 0.5877 | 0.7864 | 0.7864 | 0.7864 | 0.7864 |
| 0.9675 | 8.0 | 992 | 0.7651 | 0.6253 | 0.5880 | 0.5937 | 0.7926 | 0.7926 | 0.7926 | 0.7926 |
| 0.5065 | 9.0 | 1116 | 0.8560 | 0.6075 | 0.5930 | 0.5945 | 0.7767 | 0.7767 | 0.7767 | 0.7767 |
| 0.5065 | 10.0 | 1240 | 0.8643 | 0.6354 | 0.5842 | 0.5972 | 0.7902 | 0.7902 | 0.7902 | 0.7902 |
| 0.5065 | 11.0 | 1364 | 0.9259 | 0.6337 | 0.5884 | 0.6021 | 0.7895 | 0.7895 | 0.7895 | 0.7895 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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