---
language:
- en
widget:
- text: "Paste in a 13F Quarterly Report Here."
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-13f-reports
  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. -->

# mt5-small-finetuned-13f-reports

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4818
- Rouge1: 0.3235
- Rouge2: 0.2725
- Rougel: 0.3146
- Rougelsum: 0.3161

## Model description

More information needed

## Intended uses & limitations

The model was fine tuned on a dataset of 1000+ quarterly 13F reports. It is intended for use with automating the 
generation of summaries of articles before they are published. This allows you to put in a TL;DR summary without
having to write one on your own.

NOTE: The HuggingFace hosted Inference API interface takes the default parameters and so only outputs about 20
words of text. To get a full summary, use the Inference API directly and pass in max_length=120 or so.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 11.4662       | 1.0   | 126  | 2.9329          | 0.2023 | 0.0998 | 0.1717 | 0.1792    |
| 3.4401        | 2.0   | 252  | 1.9914          | 0.3142 | 0.2573 | 0.3015 | 0.3036    |
| 2.5139        | 3.0   | 378  | 1.7493          | 0.3131 | 0.2576 | 0.3022 | 0.3039    |
| 2.152         | 4.0   | 504  | 1.6465          | 0.3114 | 0.2564 | 0.3009 | 0.3024    |
| 1.9624        | 5.0   | 630  | 1.5607          | 0.3202 | 0.2695 | 0.3114 | 0.3127    |
| 1.851         | 6.0   | 756  | 1.5163          | 0.3205 | 0.2704 | 0.3101 | 0.311     |
| 1.8002        | 7.0   | 882  | 1.4848          | 0.3225 | 0.2718 | 0.3148 | 0.3161    |
| 1.7864        | 8.0   | 1008 | 1.4818          | 0.3235 | 0.2725 | 0.3146 | 0.3161    |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0