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---
base_model: google/flan-t5-large
datasets:
- samsum
library_name: peft
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: FlanT5Summarization-samsum
results: []
---
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/daigt_team/Summarization%20by%20Finetuning%20FlanT5-LoRA/runs/bzfwtjcj)
# FlanT5Summarization-samsum
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co./google/flan-t5-large) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3001
- Rouge1: 0.2788
- Rouge2: 0.1310
- Rougel: 0.2363
- Rougelsum: 0.2369
## 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: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.1072 | 0.0866 | 5 | 0.9165 | 0.2705 | 0.1135 | 0.2226 | 0.2229 |
| 1.1039 | 0.1732 | 10 | 0.9080 | 0.2709 | 0.1138 | 0.2230 | 0.2234 |
| 1.0848 | 0.2597 | 15 | 0.8917 | 0.2706 | 0.1137 | 0.2228 | 0.2231 |
| 1.0706 | 0.3463 | 20 | 0.8654 | 0.2709 | 0.1142 | 0.2232 | 0.2234 |
| 1.0461 | 0.4329 | 25 | 0.8336 | 0.2706 | 0.1140 | 0.2228 | 0.2232 |
| 1.0187 | 0.5195 | 30 | 0.7960 | 0.2718 | 0.1145 | 0.2240 | 0.2243 |
| 0.9774 | 0.6061 | 35 | 0.7532 | 0.2723 | 0.1152 | 0.2250 | 0.2253 |
| 0.9326 | 0.6926 | 40 | 0.7064 | 0.2726 | 0.1153 | 0.2253 | 0.2257 |
| 0.8834 | 0.7792 | 45 | 0.6570 | 0.2728 | 0.1160 | 0.2259 | 0.2261 |
| 0.833 | 0.8658 | 50 | 0.6080 | 0.2734 | 0.1161 | 0.2262 | 0.2263 |
| 0.7871 | 0.9524 | 55 | 0.5614 | 0.2726 | 0.1156 | 0.2260 | 0.2260 |
| 0.735 | 1.0390 | 60 | 0.5180 | 0.2731 | 0.1169 | 0.2262 | 0.2264 |
| 0.6978 | 1.1255 | 65 | 0.4802 | 0.2736 | 0.1179 | 0.2275 | 0.2276 |
| 0.6464 | 1.2121 | 70 | 0.4482 | 0.2741 | 0.1188 | 0.2283 | 0.2286 |
| 0.6175 | 1.2987 | 75 | 0.4222 | 0.2742 | 0.1193 | 0.2291 | 0.2292 |
| 0.5722 | 1.3853 | 80 | 0.4007 | 0.2740 | 0.1187 | 0.2287 | 0.2287 |
| 0.5443 | 1.4719 | 85 | 0.3834 | 0.2730 | 0.1180 | 0.2282 | 0.2282 |
| 0.5203 | 1.5584 | 90 | 0.3692 | 0.2740 | 0.1192 | 0.2293 | 0.2293 |
| 0.4851 | 1.6450 | 95 | 0.3568 | 0.2744 | 0.1201 | 0.2300 | 0.2302 |
| 0.4619 | 1.7316 | 100 | 0.3466 | 0.2746 | 0.1201 | 0.2304 | 0.2305 |
| 0.4484 | 1.8182 | 105 | 0.3379 | 0.2754 | 0.1218 | 0.2314 | 0.2319 |
| 0.4357 | 1.9048 | 110 | 0.3305 | 0.2766 | 0.1241 | 0.2325 | 0.2330 |
| 0.4246 | 1.9913 | 115 | 0.3243 | 0.2772 | 0.1254 | 0.2338 | 0.2341 |
| 0.4074 | 2.0779 | 120 | 0.3190 | 0.2776 | 0.1263 | 0.2343 | 0.2347 |
| 0.3965 | 2.1645 | 125 | 0.3144 | 0.2775 | 0.1264 | 0.2342 | 0.2345 |
| 0.3922 | 2.2511 | 130 | 0.3105 | 0.2776 | 0.1266 | 0.2344 | 0.2347 |
| 0.3861 | 2.3377 | 135 | 0.3073 | 0.2786 | 0.1289 | 0.2357 | 0.2362 |
| 0.382 | 2.4242 | 140 | 0.3048 | 0.2782 | 0.1289 | 0.2354 | 0.2358 |
| 0.3807 | 2.5108 | 145 | 0.3029 | 0.2787 | 0.1297 | 0.2359 | 0.2364 |
| 0.3717 | 2.5974 | 150 | 0.3016 | 0.2787 | 0.1303 | 0.2363 | 0.2367 |
| 0.3708 | 2.6840 | 155 | 0.3008 | 0.2788 | 0.1305 | 0.2363 | 0.2368 |
| 0.372 | 2.7706 | 160 | 0.3003 | 0.2789 | 0.1310 | 0.2365 | 0.2370 |
| 0.3696 | 2.8571 | 165 | 0.3002 | 0.2788 | 0.1310 | 0.2363 | 0.2369 |
| 0.3646 | 2.9437 | 170 | 0.3001 | 0.2788 | 0.1310 | 0.2363 | 0.2369 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.43.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1 |