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---
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
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