transformers tensorboard safetensors pegasus text2text-generation generated_from_trainer base_model:google/pegasus-xsum autotrain_compatible endpoints_compatible region:us
---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
model-index:
- name: pegasus-legalease
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# pegasus-legalease

This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1372

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.09  | 250  | 4.9954          |
| 5.2592        | 0.18  | 500  | 4.3175          |
| 5.2592        | 0.27  | 750  | 1.3074          |
| 2.3819        | 0.35  | 1000 | 1.1987          |
| 2.3819        | 0.44  | 1250 | 1.1678          |
| 1.3113        | 0.53  | 1500 | 1.1491          |
| 1.3113        | 0.62  | 1750 | 1.1369          |
| 1.2158        | 0.71  | 2000 | 1.1273          |
| 1.2158        | 0.8   | 2250 | 1.1165          |
| 1.2119        | 0.89  | 2500 | 1.1137          |
| 1.2119        | 0.98  | 2750 | 1.1147          |
| 1.2307        | 1.06  | 3000 | 1.1210          |
| 1.2307        | 1.15  | 3250 | 1.1246          |
| 1.2107        | 1.24  | 3500 | 1.1269          |
| 1.2107        | 1.33  | 3750 | 1.1372          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
