MarPla/LifeSciencePegasusLargeModel

1---2base_model: google/pegasus-large3tags:4- generated_from_trainer5metrics:6- rouge7- bleu8model-index:9- name: LifeSciencePegasusLargeModel10  results: []11---12 13<!-- This model card has been generated automatically according to the information the Trainer had access to. You14should probably proofread and complete it, then remove this comment. -->15 16# LifeSciencePegasusLargeModel17 18This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.19It achieves the following results on the evaluation set:20- Loss: 5.652321- Rouge1: 44.776122- Rouge2: 12.672623- Rougel: 29.084724- Rougelsum: 40.756625- Bertscore Precision: 77.928326- Bertscore Recall: 81.585427- Bertscore F1: 79.709228- Bleu: 0.088629- Gen Len: 225.722030 31## Model description32 33More information needed34 35## Intended uses & limitations36 37More information needed38 39## Training and evaluation data40 41More information needed42 43## Training procedure44 45### Training hyperparameters46 47The following hyperparameters were used during training:48- learning_rate: 5e-0549- train_batch_size: 150- eval_batch_size: 151- seed: 4252- gradient_accumulation_steps: 1653- total_train_batch_size: 1654- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0855- lr_scheduler_type: linear56- lr_scheduler_warmup_steps: 50057- num_epochs: 158 59### Training results60 61| Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu   | Gen Len  |62|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|63| 6.2586        | 0.2643 | 300  | 6.0453          | 40.1947 | 11.1082 | 26.9714 | 36.2747   | 76.6344             | 80.8385          | 78.6731      | 0.0775 | 225.7220 |64| 6.0213        | 0.5286 | 600  | 5.7899          | 43.2445 | 12.1722 | 28.4564 | 39.1524   | 77.5194             | 81.3755          | 79.3945      | 0.0856 | 225.7220 |65| 5.9018        | 0.7929 | 900  | 5.6523          | 44.7761 | 12.6726 | 29.0847 | 40.7566   | 77.9283             | 81.5854          | 79.7092      | 0.0886 | 225.7220 |66 67 68### Framework versions69 70- Transformers 4.41.271- Pytorch 2.1.272- Datasets 2.2.173- Tokenizers 0.19.174