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-3
  results: []
---

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# mbart-large-50-finetuned-stocks-event-3

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: 0.2914
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 5.4797        | 1.0   | 29   | 1.7775          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.5482        | 2.0   | 58   | 0.6397          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.3064        | 3.0   | 87   | 0.3303          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.1991        | 4.0   | 116  | 0.2728          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.1673        | 5.0   | 145  | 0.2764          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.1513        | 6.0   | 174  | 0.3056          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.145         | 7.0   | 203  | 0.2939          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0741        | 8.0   | 232  | 0.2914          | 0.0    | 0.0    | 0.0    | 0.0       |


### Framework versions

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