File size: 2,804 Bytes
fbf77e9 |
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 |
---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: Text_Summarization_model_15042024
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. -->
# Text_Summarization_model_15042024
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5948
- Rouge1: 0.2374
- Rouge2: 0.1905
- Rougel: 0.2302
- Rougelsum: 0.2302
- Gen Len: 19.0
## 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: 2e-05
- 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.4344 | 0.5 | 500 | 1.9250 | 0.2184 | 0.1678 | 0.2088 | 0.2088 | 18.9925 |
| 2.0598 | 1.0 | 1000 | 1.8118 | 0.2247 | 0.1755 | 0.2155 | 0.2155 | 18.9955 |
| 1.9648 | 1.5 | 1500 | 1.7581 | 0.2303 | 0.1802 | 0.2206 | 0.2206 | 19.0 |
| 1.9119 | 2.0 | 2000 | 1.7214 | 0.2315 | 0.1822 | 0.2221 | 0.2221 | 19.0 |
| 1.8624 | 2.5 | 2500 | 1.6953 | 0.2337 | 0.185 | 0.2253 | 0.2253 | 19.0 |
| 1.8508 | 3.0 | 3000 | 1.6769 | 0.2346 | 0.186 | 0.2266 | 0.2266 | 19.0 |
| 1.8092 | 3.5 | 3500 | 1.6563 | 0.2353 | 0.1871 | 0.2278 | 0.2279 | 19.0 |
| 1.8065 | 4.0 | 4000 | 1.6377 | 0.2359 | 0.188 | 0.2284 | 0.2284 | 19.0 |
| 1.7724 | 4.5 | 4500 | 1.6309 | 0.237 | 0.1895 | 0.2297 | 0.2298 | 19.0 |
| 1.7703 | 5.0 | 5000 | 1.6165 | 0.2376 | 0.1899 | 0.2302 | 0.2303 | 19.0 |
| 1.7468 | 5.5 | 5500 | 1.6082 | 0.2374 | 0.1902 | 0.2303 | 0.2303 | 19.0 |
| 1.7347 | 6.0 | 6000 | 1.5992 | 0.2374 | 0.1906 | 0.2303 | 0.2304 | 19.0 |
| 1.7162 | 6.5 | 6500 | 1.5948 | 0.2374 | 0.1905 | 0.2302 | 0.2302 | 19.0 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|