File size: 1,938 Bytes
1cc1ad0 2c72e41 1cc1ad0 2c72e41 1cc1ad0 2c72e41 1cc1ad0 2c72e41 1cc1ad0 |
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 |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-base-dialogsum-summarization
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. -->
# flan-t5-base-dialogsum-summarization
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2095
- Rouge1: 39.3212
- Rouge2: 15.6335
- Rougel: 33.4773
- Rougelsum: 35.1795
- Gen Len: 18.872
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.1318 | 1.0 | 1558 | 1.2331 | 39.1301 | 15.2555 | 33.1115 | 35.0288 | 18.868 |
| 1.0483 | 2.0 | 3116 | 1.2095 | 39.3212 | 15.6335 | 33.4773 | 35.1795 | 18.872 |
| 0.9969 | 3.0 | 4674 | 1.2104 | 40.0115 | 16.029 | 34.0364 | 35.8358 | 18.852 |
| 0.9601 | 4.0 | 6232 | 1.2161 | 39.7403 | 15.9708 | 33.8644 | 35.5952 | 18.868 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1
|