---
base_model: allenai/PRIMERA-multinews
library_name: peft
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: PRIMERA-multinews-lora-finetuned
  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. -->

# PRIMERA-multinews-lora-finetuned

This model is a fine-tuned version of [allenai/PRIMERA-multinews](https://huggingface.co./allenai/PRIMERA-multinews) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6767
- Rouge1: 13.1661
- Rouge2: 6.075
- Rougel: 11.1948
- Rougelsum: 12.1382
- Gen Len: 20.0

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.2385        | 1.0   | 4725  | 1.9183          | 15.8308 | 7.6039 | 12.6624 | 14.1866   | 20.0    |
| 2.1419        | 2.0   | 9450  | 1.8933          | 14.8545 | 6.8893 | 12.144  | 13.4623   | 20.0    |
| 2.1286        | 3.0   | 14175 | 1.8619          | 16.2585 | 8.1431 | 13.4226 | 14.8653   | 20.0    |
| 2.0669        | 4.0   | 18900 | 1.8129          | 15.9624 | 7.5293 | 13.3809 | 14.7765   | 20.0    |
| 2.0448        | 5.0   | 23625 | 1.7636          | 16.2801 | 8.045  | 13.6178 | 14.996    | 20.0    |
| 1.9831        | 6.0   | 28350 | 1.7037          | 13.8735 | 5.9956 | 10.8251 | 12.2545   | 20.0    |
| 1.9926        | 7.0   | 33075 | 1.7623          | 13.9591 | 5.8861 | 11.112  | 12.4349   | 20.0    |
| 1.99          | 8.0   | 37800 | 1.7247          | 13.1441 | 5.2565 | 10.7117 | 11.851    | 20.0    |
| 1.9495        | 9.0   | 42525 | 1.7065          | 12.4863 | 4.6444 | 10.0155 | 11.3874   | 20.0    |
| 1.9782        | 10.0  | 47250 | 1.6919          | 11.8394 | 4.0068 | 9.4554  | 10.6421   | 20.0    |
| 1.9087        | 11.0  | 51975 | 1.6910          | 13.011  | 5.5644 | 10.7255 | 11.8532   | 20.0    |
| 1.9693        | 12.0  | 56700 | 1.6872          | 13.2678 | 5.7966 | 11.0537 | 12.1103   | 20.0    |
| 1.9445        | 13.0  | 61425 | 1.7084          | 13.2757 | 5.9337 | 11.084  | 12.2354   | 20.0    |
| 1.9467        | 14.0  | 66150 | 1.6729          | 12.9202 | 5.424  | 10.5315 | 11.6661   | 20.0    |
| 1.9582        | 15.0  | 70875 | 1.6786          | 13.2851 | 6.0806 | 11.291  | 12.2518   | 20.0    |
| 1.9186        | 16.0  | 75600 | 1.6767          | 13.1661 | 6.075  | 11.1948 | 12.1382   | 20.0    |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.2
- Pytorch 2.2.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1