File size: 2,219 Bytes
7159c81 eb85a97 7159c81 eb85a97 7159c81 eb85a97 7159c81 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
---
base_model: facebook/bart-base
library_name: peft
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: bart-base-summarization-medical_on_cnn-50
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-base-summarization-medical_on_cnn-50
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3948
- Rouge1: 0.2474
- Rouge2: 0.0918
- Rougel: 0.1969
- Rougelsum: 0.2198
- Gen Len: 18.166
## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 50
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.7028 | 1.0 | 1250 | 3.3697 | 0.25 | 0.0901 | 0.1969 | 0.2216 | 18.807 |
| 2.5838 | 2.0 | 2500 | 3.3865 | 0.2496 | 0.0904 | 0.196 | 0.2207 | 18.586 |
| 2.5612 | 3.0 | 3750 | 3.3752 | 0.2511 | 0.0928 | 0.1984 | 0.2223 | 18.311 |
| 2.5589 | 4.0 | 5000 | 3.3884 | 0.2508 | 0.0931 | 0.1991 | 0.2222 | 18.33 |
| 2.5524 | 5.0 | 6250 | 3.3936 | 0.2477 | 0.0931 | 0.1967 | 0.2204 | 18.159 |
| 2.5314 | 6.0 | 7500 | 3.3948 | 0.2474 | 0.0918 | 0.1969 | 0.2198 | 18.166 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |