---
license: mit
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mbart-large-50-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. -->

# mbart-large-50-finetuned-stocks-event-all

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5518
- Rouge1: 0.5383
- Rouge2: 0.4868
- Rougel: 0.5387
- Rougelsum: 0.5362

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.2097        | 1.0   | 97   | 0.5821          | 0.5174 | 0.4646 | 0.5137 | 0.5111    |
| 0.5315        | 2.0   | 194  | 0.4826          | 0.5169 | 0.4709 | 0.5186 | 0.5168    |
| 0.3602        | 3.0   | 291  | 0.4677          | 0.5319 | 0.4811 | 0.5344 | 0.5304    |
| 0.2639        | 4.0   | 388  | 0.4724          | 0.5319 | 0.4750 | 0.5335 | 0.5318    |
| 0.1715        | 5.0   | 485  | 0.4504          | 0.5331 | 0.4790 | 0.5337 | 0.5323    |
| 0.1136        | 6.0   | 582  | 0.4894          | 0.5321 | 0.4886 | 0.5324 | 0.5295    |
| 0.0618        | 7.0   | 679  | 0.5445          | 0.5456 | 0.4959 | 0.5473 | 0.5438    |
| 0.0347        | 8.0   | 776  | 0.5518          | 0.5383 | 0.4868 | 0.5387 | 0.5362    |


### Framework versions

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