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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- stackexchange_titlebody_best_voted_answer_jsonl
metrics:
- rouge
model-index:
- name: flan-t5-base-flant5-apple-support
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: stackexchange_titlebody_best_voted_answer_jsonl
type: stackexchange_titlebody_best_voted_answer_jsonl
config: apple
split: train[:10%]
args: apple
metrics:
- name: Rouge1
type: rouge
value: 12.7991
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# flan-t5-base-flant5-apple-support
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the stackexchange_titlebody_best_voted_answer_jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9676
- Rouge1: 12.7991
- Rouge2: 2.244
- Rougel: 9.8075
- Rougelsum: 11.3618
- Gen Len: 18.9087
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 3.2673 | 1.0 | 1157 | 3.0350 | 12.4094 | 2.1794 | 9.5255 | 10.9739 | 18.9723 |
| 3.1854 | 2.0 | 2314 | 2.9992 | 12.4579 | 2.1512 | 9.5232 | 11.0049 | 18.9647 |
| 3.1006 | 3.0 | 3471 | 2.9792 | 12.9794 | 2.2794 | 9.9245 | 11.5019 | 18.9436 |
| 3.0751 | 4.0 | 4628 | 2.9711 | 12.6779 | 2.1828 | 9.6962 | 11.221 | 18.9137 |
| 3.0532 | 5.0 | 5785 | 2.9676 | 12.7991 | 2.244 | 9.8075 | 11.3618 | 18.9087 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2