File size: 2,033 Bytes
2eac4e1 |
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 |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: t5-small-summarization-samsum
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. -->
# t5-small-summarization-samsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7907
- Rouge1: 0.4318
- Rouge2: 0.2005
- Rougel: 0.3629
- Rougelsum: 0.3629
- Gen Len: 16.8971
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.1074 | 0.54 | 500 | 1.9230 | 0.4011 | 0.182 | 0.3418 | 0.3417 | 15.7439 |
| 2.0526 | 1.09 | 1000 | 1.8559 | 0.4122 | 0.1841 | 0.3478 | 0.348 | 16.386 |
| 2.0075 | 1.63 | 1500 | 1.8193 | 0.4273 | 0.1955 | 0.3552 | 0.3551 | 16.8554 |
| 1.97 | 2.17 | 2000 | 1.8086 | 0.4222 | 0.1922 | 0.3551 | 0.3552 | 16.761 |
| 1.931 | 2.72 | 2500 | 1.7907 | 0.4318 | 0.2005 | 0.3629 | 0.3629 | 16.8971 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|