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---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-gen-12-small_dataset
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. -->
[<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/greatakela/gen_chatbot_models/runs/g7jitsum)
# flan-t5-base-gen-12-small_dataset
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1621
- Rouge 1: 7.3814
- Rouge 2: 0.6192
- Rouge L: 6.8531
- Avg Len: 13.0278
- Bertscore Prec: 0.8612
- Bertscore Rec: 0.8542
- Bertscore F1: 0.8573
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge 1 | Rouge 2 | Rouge L | Avg Len | Bertscore Prec | Bertscore Rec | Bertscore F1 |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:--------------:|:-------------:|:------------:|
| 3.8134 | 0.6173 | 200 | 3.4410 | 6.2979 | 0.223 | 5.7832 | 13.5052 | 0.8507 | 0.8498 | 0.8498 |
| 3.5423 | 1.2346 | 400 | 3.3112 | 6.0189 | 0.3369 | 5.6265 | 14.6944 | 0.8611 | 0.8514 | 0.8558 |
| 3.3863 | 1.8519 | 600 | 3.2457 | 5.8478 | 0.312 | 5.5206 | 14.901 | 0.8649 | 0.8522 | 0.8581 |
| 3.2873 | 2.4691 | 800 | 3.2077 | 6.1468 | 0.4176 | 5.7813 | 14.4757 | 0.8643 | 0.8522 | 0.8578 |
| 3.2097 | 3.0864 | 1000 | 3.1873 | 6.8407 | 0.5555 | 6.391 | 13.6875 | 0.8591 | 0.8521 | 0.8553 |
| 3.1199 | 3.7037 | 1200 | 3.1723 | 6.6644 | 0.3774 | 6.2188 | 15.6545 | 0.8557 | 0.8511 | 0.8531 |
| 3.0885 | 4.3210 | 1400 | 3.1635 | 7.0627 | 0.5238 | 6.5367 | 14.4826 | 0.861 | 0.8527 | 0.8565 |
| 3.033 | 4.9383 | 1600 | 3.1565 | 7.0399 | 0.5467 | 6.4524 | 14.401 | 0.8596 | 0.8527 | 0.8558 |
| 2.9712 | 5.5556 | 1800 | 3.1555 | 7.1467 | 0.5327 | 6.4363 | 14.6406 | 0.8566 | 0.853 | 0.8545 |
| 2.9196 | 6.1728 | 2000 | 3.1563 | 7.1535 | 0.4741 | 6.6271 | 14.8073 | 0.8558 | 0.8531 | 0.8542 |
| 2.8896 | 6.7901 | 2200 | 3.1531 | 7.1215 | 0.5534 | 6.5025 | 14.408 | 0.8579 | 0.853 | 0.8551 |
| 2.8631 | 7.4074 | 2400 | 3.1547 | 7.4895 | 0.7019 | 6.8118 | 14.092 | 0.8581 | 0.8533 | 0.8554 |
| 2.8525 | 8.0247 | 2600 | 3.1532 | 7.1931 | 0.6333 | 6.6858 | 13.9201 | 0.8586 | 0.8528 | 0.8553 |
| 2.7951 | 8.6420 | 2800 | 3.1546 | 7.2016 | 0.7094 | 6.6671 | 13.4878 | 0.8599 | 0.8534 | 0.8563 |
| 2.7996 | 9.2593 | 3000 | 3.1568 | 7.225 | 0.6035 | 6.7029 | 13.724 | 0.8582 | 0.8532 | 0.8554 |
| 2.7721 | 9.8765 | 3200 | 3.1563 | 7.0646 | 0.6486 | 6.5622 | 13.125 | 0.8602 | 0.853 | 0.8562 |
| 2.759 | 10.4938 | 3400 | 3.1625 | 7.3836 | 0.7279 | 6.9035 | 12.6927 | 0.8613 | 0.8535 | 0.857 |
| 2.7459 | 11.1111 | 3600 | 3.1600 | 7.4314 | 0.6359 | 6.8986 | 13.1528 | 0.8605 | 0.8539 | 0.8569 |
| 2.7356 | 11.7284 | 3800 | 3.1621 | 7.3814 | 0.6192 | 6.8531 | 13.0278 | 0.8612 | 0.8542 | 0.8573 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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