---
library_name: transformers
base_model: MHGanainy/xmod-roberta-base-legal-multi
tags:
- generated_from_trainer
model-index:
- name: xmod-roberta-base-legal-multi-ecthr-downstream-ecthr-a
  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-roberta-base-legal-multi-ecthr-downstream-ecthr-a

This model is a fine-tuned version of [MHGanainy/xmod-roberta-base-legal-multi](https://huggingface.co/MHGanainy/xmod-roberta-base-legal-multi) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2232
- Macro-f1: 0.6306
- Micro-f1: 0.6835

## 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: 16
- eval_batch_size: 16
- seed: 1
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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 | Macro-f1 | Micro-f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| No log        | 1.0   | 282  | 0.1829          | 0.5518   | 0.6772   |
| 0.1547        | 2.0   | 564  | 0.1643          | 0.5833   | 0.6849   |
| 0.1547        | 3.0   | 846  | 0.1821          | 0.6056   | 0.6864   |
| 0.1031        | 4.0   | 1128 | 0.1714          | 0.6432   | 0.7086   |
| 0.1031        | 5.0   | 1410 | 0.1662          | 0.6357   | 0.6958   |
| 0.0836        | 6.0   | 1692 | 0.1835          | 0.6309   | 0.6896   |
| 0.0836        | 7.0   | 1974 | 0.2232          | 0.6306   | 0.6835   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1