transformers safetensors led text2text-generation summarization base_model:nsi319/legal-led-base-16384 base_model:finetune:nsi319/legal-led-base-16384 license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: nsi319/legal-led-base-16384
library_name: transformers
license: mit
pipeline_tag: summarization
model-index:
- name: results
  results: []
---

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# results

This model is a fine-tuned version of [nsi319/legal-led-base-16384](https://huggingface.co/nsi319/legal-led-base-16384) on the joelniklaus/legal_case_document_summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7401

## 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: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2           | 1.0   | 1924 | 2.8550          |
| 3.6193        | 2.0   | 3848 | 2.7593          |
| 2.7776        | 3.0   | 5772 | 2.7401          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0