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---
license: apache-2.0
base_model: allenai/led-base-16384
tags:
- generated_from_trainer
datasets:
- xlsum-fi
model-index:
- name: allenai/led-base-16384
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. -->
# allenai/led-base-16384
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the xlsum-fi dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3962
- Rouge2 Precision: 0.0109
- Rouge2 Recall: 0.0248
- Rouge2 Fmeasure: 0.0152
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 3.8391 | 0.32 | 10 | 3.5714 | 0.0062 | 0.016 | 0.0089 |
| 3.8 | 0.64 | 20 | 3.4777 | 0.0083 | 0.0202 | 0.0115 |
| 3.6502 | 0.96 | 30 | 3.3962 | 0.0109 | 0.0248 | 0.0152 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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