---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-gen-chat_base-5
  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/cnho9icy)
# flan-t5-base-gen-chat_base-5

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: 1.2877
- Rouge 1: 13.4731
- Rouge 2: 4.141
- Rouge L: 12.5428
- Avg Len: 12.3875
- Bertscore Prec: 0.8648
- Bertscore Rec: 0.8591
- Bertscore F1: 0.8616

## 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: 5

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Rouge 1 | Rouge 2 | Rouge L | Avg Len | Bertscore Prec | Bertscore Rec | Bertscore F1 |
|:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:--------------:|:-------------:|:------------:|
| 3.602         | 0.1771 | 400   | 3.3043          | 6.1296  | 0.3906  | 5.6284  | 13.5263 | 0.8505         | 0.8478        | 0.8488       |
| 3.3799        | 0.3543 | 800   | 3.0822          | 6.6949  | 0.4835  | 6.216   | 15.4916 | 0.857          | 0.8507        | 0.8535       |
| 3.1711        | 0.5314 | 1200  | 2.8825          | 6.284   | 0.4519  | 5.9964  | 16.6215 | 0.8625         | 0.8513        | 0.8566       |
| 3.0298        | 0.7086 | 1600  | 2.7078          | 6.7319  | 0.5659  | 6.3339  | 16.9338 | 0.8602         | 0.8527        | 0.8562       |
| 2.8722        | 0.8857 | 2000  | 2.5615          | 7.9438  | 0.9859  | 7.4262  | 15.1393 | 0.8622         | 0.8538        | 0.8577       |
| 2.7398        | 1.0629 | 2400  | 2.4291          | 8.3717  | 0.9243  | 7.7474  | 14.9595 | 0.8601         | 0.854         | 0.8568       |
| 2.6196        | 1.2400 | 2800  | 2.3052          | 8.8458  | 1.117   | 8.2482  | 14.5368 | 0.8616         | 0.8544        | 0.8577       |
| 2.5076        | 1.4172 | 3200  | 2.1998          | 9.1369  | 1.2588  | 8.5322  | 14.2939 | 0.8626         | 0.855         | 0.8585       |
| 2.4567        | 1.5943 | 3600  | 2.1053          | 9.6663  | 1.4424  | 8.9702  | 12.928  | 0.8634         | 0.8547        | 0.8587       |
| 2.3665        | 1.7715 | 4000  | 2.0065          | 9.7718  | 1.4302  | 9.0438  | 13.2387 | 0.8622         | 0.8554        | 0.8585       |
| 2.3142        | 1.9486 | 4400  | 1.9322          | 10.2579 | 1.7969  | 9.5247  | 12.6887 | 0.8629         | 0.8558        | 0.859        |
| 2.1932        | 2.1258 | 4800  | 1.8453          | 10.4199 | 1.9269  | 9.6524  | 12.3864 | 0.8611         | 0.8553        | 0.8578       |
| 2.1476        | 2.3029 | 5200  | 1.7822          | 10.798  | 2.141   | 9.9692  | 12.5258 | 0.8617         | 0.8562        | 0.8586       |
| 2.1042        | 2.4801 | 5600  | 1.7094          | 10.9928 | 2.3716  | 10.1852 | 12.5231 | 0.8623         | 0.8564        | 0.859        |
| 2.0594        | 2.6572 | 6000  | 1.6480          | 11.5976 | 2.3487  | 10.6123 | 12.7434 | 0.8629         | 0.8573        | 0.8598       |
| 2.0017        | 2.8344 | 6400  | 1.5988          | 12.0383 | 2.689   | 11.044  | 12.582  | 0.8632         | 0.8577        | 0.8601       |
| 1.967         | 3.0115 | 6800  | 1.5445          | 11.8601 | 3.0155  | 10.9982 | 12.438  | 0.8636         | 0.8579        | 0.8604       |
| 1.9023        | 3.1887 | 7200  | 1.4983          | 12.1844 | 3.2931  | 11.3648 | 12.04   | 0.864          | 0.8575        | 0.8604       |
| 1.8604        | 3.3658 | 7600  | 1.4650          | 12.498  | 3.2698  | 11.5864 | 12.5668 | 0.8635         | 0.8586        | 0.8607       |
| 1.8345        | 3.5430 | 8000  | 1.4231          | 12.7205 | 3.6395  | 11.7643 | 12.4364 | 0.8644         | 0.8591        | 0.8614       |
| 1.8117        | 3.7201 | 8400  | 1.3965          | 12.6639 | 3.6483  | 11.824  | 12.4795 | 0.864          | 0.8585        | 0.8609       |
| 1.7999        | 3.8973 | 8800  | 1.3652          | 13.0487 | 3.7723  | 12.1147 | 12.4784 | 0.8643         | 0.8588        | 0.8612       |
| 1.7583        | 4.0744 | 9200  | 1.3443          | 13.2051 | 3.807   | 12.2615 | 12.4264 | 0.865          | 0.8591        | 0.8617       |
| 1.7608        | 4.2516 | 9600  | 1.3277          | 13.1401 | 3.7791  | 12.2312 | 12.4989 | 0.8644         | 0.859         | 0.8613       |
| 1.7111        | 4.4287 | 10000 | 1.3100          | 12.9527 | 3.7422  | 12.0599 | 12.4911 | 0.8641         | 0.8587        | 0.861        |
| 1.7137        | 4.6058 | 10400 | 1.2955          | 13.1295 | 4.0091  | 12.2326 | 12.5794 | 0.864          | 0.8588        | 0.8611       |
| 1.712         | 4.7830 | 10800 | 1.2899          | 13.511  | 4.1509  | 12.574  | 12.3586 | 0.865          | 0.8593        | 0.8618       |
| 1.6791        | 4.9601 | 11200 | 1.2877          | 13.4731 | 4.141   | 12.5428 | 12.3875 | 0.8648         | 0.8591        | 0.8616       |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1