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---
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
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