transformers tensorboard safetensors bart text2text-generation summarization generated_from_trainer base_model:october-sd/bart-large-xsum-finetuned-en-sum base_model:finetune:october-sd/bart-large-xsum-finetuned-en-sum license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: october-sd/bart-large-xsum-finetuned-en-sum
license: mit
tags:
- summarization
- generated_from_trainer
model-index:
- name: bart-large-xsum-finetuned-en-sum-2
  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. -->

# bart-large-xsum-finetuned-en-sum-2

This model is a fine-tuned version of [october-sd/bart-large-xsum-finetuned-en-sum](https://huggingface.co/october-sd/bart-large-xsum-finetuned-en-sum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6259

## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- 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        | 0.99  | 126  | 1.6222          |
| No log        | 1.98  | 252  | 1.6259          |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.2
