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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- shared_TaskA
metrics:
- rouge
model-index:
- name: flan-t5-base-dialogue
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: shared_TaskA
type: shared_TaskA
config: shared_TaskA
split: train
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 28.1748
---
<!-- 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. -->
# flan-t5-base-sharedTaskA
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the shared_TaskA dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5153
- Rouge1: 28.1748
- Rouge2: 14.384
- Rougel: 27.6673
- Rougelsum: 27.8465
- Gen Len: 18.85
## 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 Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 2.554769 27.797100 14.471000 27.468300 27.617000 18.970000
No log 2.515381 28.174800 14.384000 27.667300 27.846500 18.850000
No log 2.542737 27.982600 14.754000 27.559000 27.834200 18.800000
1.809200 2.528819 28.010600 15.268300 27.816000 27.999000 18.690000
1.809200 2.534979 28.104800 15.248000 27.840400 28.069500 18.670000
### Example Uses
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer_pre = AutoTokenizer.from_pretrained("Amalq/flan-t5-dialogue")
model_pre = AutoModelForSeq2SeqLM.from_pretrained("Amalq/flan-t5-dialogue")
```
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