---
license: mit
base_model: haining/scientific_abstract_simplification
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: SAS-finetuned-cochrane-medeasi
  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. -->

# SAS-finetuned-cochrane-medeasi

This model is a fine-tuned version of [haining/scientific_abstract_simplification](https://huggingface.co/haining/scientific_abstract_simplification) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Bleu: {'bleu': 3.5213074954706223e-06, 'precisions': [0.49951576455396535, 0.15234465234465233, 0.06880219369313224, 0.036816459122902004], 'brevity_penalty': 2.9884691172035265e-05, 'length_ratio': 0.08757975289560735, 'translation_length': 9293, 'reference_length': 106109}
- Sari: {'sari': 2.5441859559296094}

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu                                                                                                                                                                                                                                                                          | Sari                         |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------:|
| No log        | 1.0   | 159  | nan             | {'bleu': 3.5213074954706223e-06, 'precisions': [0.49951576455396535, 0.15234465234465233, 0.06880219369313224, 0.036816459122902004], 'brevity_penalty': 2.9884691172035265e-05, 'length_ratio': 0.08757975289560735, 'translation_length': 9293, 'reference_length': 106109} | {'sari': 2.5441859559296094} |
| No log        | 2.0   | 318  | nan             | {'bleu': 3.5213074954706223e-06, 'precisions': [0.49951576455396535, 0.15234465234465233, 0.06880219369313224, 0.036816459122902004], 'brevity_penalty': 2.9884691172035265e-05, 'length_ratio': 0.08757975289560735, 'translation_length': 9293, 'reference_length': 106109} | {'sari': 2.5441859559296094} |
| No log        | 3.0   | 477  | nan             | {'bleu': 3.5213074954706223e-06, 'precisions': [0.49951576455396535, 0.15234465234465233, 0.06880219369313224, 0.036816459122902004], 'brevity_penalty': 2.9884691172035265e-05, 'length_ratio': 0.08757975289560735, 'translation_length': 9293, 'reference_length': 106109} | {'sari': 2.5441859559296094} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1