transformers pytorch tensorboard mbart text2text-generation summarization generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mbart-large-50-finetuned-stocks-event-2
  results: []
---

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# mbart-large-50-finetuned-stocks-event-2

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: 0.1281
- Rouge1: 0.9005
- Rouge2: 0.8194
- Rougel: 0.9005
- Rougelsum: 0.9005

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.9121        | 1.0   | 20   | 1.1223          | 0.1389 | 0.1111 | 0.1366 | 0.1377    |
| 0.2649        | 2.0   | 40   | 0.1712          | 0.8218 | 0.6944 | 0.8241 | 0.8194    |
| 0.0404        | 3.0   | 60   | 0.1892          | 0.9329 | 0.8611 | 0.9329 | 0.9329    |
| 0.0176        | 4.0   | 80   | 0.1553          | 0.9236 | 0.8472 | 0.9236 | 0.9213    |
| 0.0151        | 5.0   | 100  | 0.1848          | 0.8426 | 0.7454 | 0.8417 | 0.8426    |
| 0.0117        | 6.0   | 120  | 0.1917          | 0.8727 | 0.7778 | 0.8727 | 0.8727    |
| 0.0246        | 7.0   | 140  | 0.1366          | 0.9074 | 0.8333 | 0.9074 | 0.9074    |
| 0.0018        | 8.0   | 160  | 0.1281          | 0.9005 | 0.8194 | 0.9005 | 0.9005    |


### Framework versions

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