---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-stocks-event-all
  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-base-finetuned-stocks-event-all

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6143
- Rouge1: 0.4336
- Rouge2: 0.3906
- Rougel: 0.4328
- Rougelsum: 0.4317

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 6.4821        | 1.0   | 97   | 2.0502          | 0.1648 | 0.0891 | 0.1587 | 0.1588    |
| 2.3093        | 2.0   | 194  | 0.9278          | 0.3059 | 0.2526 | 0.3063 | 0.3048    |
| 1.5634        | 3.0   | 291  | 0.7646          | 0.3348 | 0.2917 | 0.3334 | 0.3334    |
| 1.2392        | 4.0   | 388  | 0.7398          | 0.3721 | 0.3193 | 0.3725 | 0.3721    |
| 1.1755        | 5.0   | 485  | 0.6872          | 0.3498 | 0.2990 | 0.3496 | 0.3507    |
| 1.0981        | 6.0   | 582  | 0.6685          | 0.3579 | 0.3256 | 0.3591 | 0.3566    |
| 1.0238        | 7.0   | 679  | 0.6379          | 0.4261 | 0.3882 | 0.4278 | 0.4253    |
| 1.0265        | 8.0   | 776  | 0.6143          | 0.4336 | 0.3906 | 0.4328 | 0.4317    |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2