---
tags:
- generated_from_trainer
base_model: facebook/mbart-large-50-many-to-many-mmt
metrics:
- rouge
model-index:
- name: summarizer-tamil-mbart
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/codebot/bart_tam_sum/runs/r6mx7h63)
# summarizer-tamil-mbart

This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4252
- Rouge1: 9.3056
- Rouge2: 2.0
- Rougel: 9.2889
- Rougelsum: 9.2222
- Gen Len: 39.2233

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch  | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:------:|:----:|:-------:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.5487        | 0.2963 | 200  | 39.11   | 4.1027          | 2.1667 | 0.3333 | 2.1111 | 2.1667    |
| 4.0997        | 0.5926 | 400  | 41.7633 | 3.9744          | 2.2222 | 0.3333 | 2.2593 | 2.2222    |
| 4.0165        | 0.8889 | 600  | 52.0967 | 3.9417          | 2.6667 | 0.6667 | 2.6667 | 2.7778    |
| 3.7801        | 1.1852 | 800  | 45.3967 | 3.9424          | 2.1444 | 0.0    | 2.2222 | 2.1333    |
| 3.7308        | 1.4815 | 1000 | 41.3833 | 3.9573          | 2.7333 | 0.2222 | 2.6063 | 2.7905    |
| 3.7946        | 1.7778 | 1200 | 35.37   | 3.8979          | 1.0571 | 0.2222 | 0.9619 | 1.0571    |
| 3.6338        | 2.0741 | 1400 | 30.9567 | 3.9569          | 1.6611 | 0.3333 | 1.6333 | 1.6833    |
| 3.2282        | 2.3704 | 1600 | 42.4933 | 3.0726          | 4.0698 | 0.3889 | 3.9754 | 3.9825    |
| 3.1351        | 2.6667 | 1800 | 38.48   | 3.0771          | 2.8333 | 0.0    | 2.8095 | 2.8333    |
| 3.1739        | 2.9630 | 2000 | 40.04   | 3.0871          | 2.4921 | 0.0    | 2.496  | 2.4762    |
| 2.8247        | 3.2593 | 2200 | 39.95   | 3.0882          | 3.4706 | 0.2222 | 3.4421 | 3.4357    |
| 2.7748        | 3.5556 | 2400 | 38.29   | 3.0735          | 3.0    | 0.0    | 3.0    | 3.0       |
| 2.5244        | 3.8519 | 2600 | 2.4450  | 7.3889          | 1.2222 | 7.4667 | 7.5    | 38.1767   |
| 2.5382        | 4.1481 | 2800 | 2.4365  | 8.1111          | 1.9744 | 8.2111 | 8.1667 | 39.3333   |
| 2.4642        | 4.4444 | 3000 | 2.4334  | 8.3889          | 2.1905 | 8.5389 | 8.4444 | 37.7767   |
| 2.4641        | 4.7407 | 3200 | 2.4252  | 9.3056          | 2.0    | 9.2889 | 9.2222 | 39.2233   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1