---
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
  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/vm7l59iq)
[<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/pzbcxrpy)
# flan-t5-base-gen-chat

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: 3.2310
- Rouge 1: 9.4633
- Rouge 2: 1.039
- Rouge L: 8.7585
- Avg Len: 12.3194
- Bertscore Prec: 0.8624
- Bertscore Rec: 0.8561
- Bertscore F1: 0.8589

## 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:--------------:|:-------------:|:------------:|
| 3.7376        | 0.5848 | 200  | 3.4194          | 7.4287  | 0.8472  | 6.9898  | 9.809   | 0.8633         | 0.8536        | 0.8581       |
| 3.4588        | 1.1696 | 400  | 3.3339          | 7.1926  | 0.8083  | 6.7158  | 9.6285  | 0.867          | 0.8546        | 0.8604       |
| 3.3367        | 1.7544 | 600  | 3.2864          | 8.7272  | 1.1167  | 8.0103  | 11.9792 | 0.8638         | 0.8565        | 0.8598       |
| 3.2902        | 2.3392 | 800  | 3.2639          | 8.5549  | 1.1047  | 7.9038  | 10.4861 | 0.8693         | 0.8558        | 0.8622       |
| 3.2053        | 2.9240 | 1000 | 3.2464          | 8.715   | 1.0443  | 8.0952  | 10.9375 | 0.8667         | 0.8558        | 0.8609       |
| 3.139         | 3.5088 | 1200 | 3.2420          | 9.1545  | 1.1215  | 8.4443  | 12.0035 | 0.8651         | 0.8571        | 0.8608       |
| 3.1294        | 4.0936 | 1400 | 3.2318          | 9.1819  | 1.0984  | 8.5163  | 11.9028 | 0.8634         | 0.8554        | 0.859        |
| 3.0969        | 4.6784 | 1600 | 3.2310          | 9.4633  | 1.039   | 8.7585  | 12.3194 | 0.8624         | 0.8561        | 0.8589       |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1