---
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")
```