---
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