File size: 2,793 Bytes
91b7272 |
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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
---
base_model: google-t5/t5-base
datasets:
- Andyrasika/TweetSumm-tuned
library_name: peft
license: apache-2.0
metrics:
- rouge
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: t5-base-ia3-finetune-tweetsumm-1724827331
results:
- task:
type: summarization
name: Summarization
dataset:
name: Andyrasika/TweetSumm-tuned
type: Andyrasika/TweetSumm-tuned
metrics:
- type: rouge
value: 0.4407
name: Rouge1
- type: f1
value: 0.8906
name: F1
- type: precision
value: 0.8894
name: Precision
- type: recall
value: 0.8921
name: Recall
---
<!-- 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-base-ia3-finetune-tweetsumm-1724827331
This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the Andyrasika/TweetSumm-tuned dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8276
- Rouge1: 0.4407
- Rouge2: 0.1997
- Rougel: 0.3672
- Rougelsum: 0.4075
- Gen Len: 49.5727
- F1: 0.8906
- Precision: 0.8894
- Recall: 0.8921
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:|
| 2.2511 | 1.0 | 879 | 1.9364 | 0.4398 | 0.1855 | 0.3668 | 0.411 | 49.5182 | 0.8883 | 0.8875 | 0.8892 |
| 1.4557 | 2.0 | 1758 | 1.8611 | 0.4491 | 0.2031 | 0.3721 | 0.4148 | 49.6091 | 0.8901 | 0.8889 | 0.8915 |
| 1.8149 | 3.0 | 2637 | 1.8386 | 0.4436 | 0.2001 | 0.3707 | 0.4092 | 49.5636 | 0.8905 | 0.889 | 0.8923 |
| 2.7192 | 4.0 | 3516 | 1.8271 | 0.4366 | 0.1966 | 0.3643 | 0.4041 | 49.6091 | 0.8897 | 0.8878 | 0.8917 |
| 1.7838 | 5.0 | 4395 | 1.8276 | 0.4407 | 0.1997 | 0.3672 | 0.4075 | 49.5727 | 0.8906 | 0.8894 | 0.8921 |
### Framework versions
- PEFT 0.12.1.dev0
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1 |