---
license: apache-2.0
base_model: gsarti/it5-small
tags:
- generated_from_trainer
datasets:
- geopolitica
metrics:
- rouge
model-index:
- name: my_awesome_geopolitical_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: geopolitica
      type: geopolitica
      config: default
      split: train
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.1409
---

<!-- 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. -->

# my_awesome_geopolitical_model

This model is a fine-tuned version of [gsarti/it5-small](https://huggingface.co/gsarti/it5-small) on the geopolitica dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.1409
- Rouge2: 0.0203
- Rougel: 0.1247
- Rougelsum: 0.125
- Gen Len: 18.781

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 53   | nan             | 0.1409 | 0.0203 | 0.1247 | 0.125     | 18.781  |
| No log        | 2.0   | 106  | nan             | 0.1409 | 0.0203 | 0.1247 | 0.125     | 18.781  |
| No log        | 3.0   | 159  | nan             | 0.1409 | 0.0203 | 0.1247 | 0.125     | 18.781  |
| No log        | 4.0   | 212  | nan             | 0.1409 | 0.0203 | 0.1247 | 0.125     | 18.781  |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3