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

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: 0.1960
- Macro-f1: 0.7411
- Micro-f1: 0.7906

## 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  | 0.1859          | 0.6611   | 0.7679   |
| 0.1734        | 2.0   | 564  | 0.2046          | 0.6510   | 0.7552   |
| 0.1734        | 3.0   | 846  | 0.1744          | 0.7103   | 0.7856   |
| 0.1145        | 4.0   | 1128 | 0.1802          | 0.7392   | 0.7924   |
| 0.1145        | 5.0   | 1410 | 0.1920          | 0.7215   | 0.7874   |
| 0.0906        | 6.0   | 1692 | 0.1912          | 0.7337   | 0.7867   |
| 0.0906        | 7.0   | 1974 | 0.1960          | 0.7411   | 0.7906   |


### Framework versions

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