
### Usage

```
from transformers import pipeline

summarizer = pipeline("summarization", model="oguuzhansahin/flan-t5-large-samsum", device=0)

sample_dialogue = "Barbara: got everything?
Haylee: yeah almost
Haylee: i'm in dairy section
Haylee: but can't find this youghurt u wanted
Barbara: the coconut milk one? Haylee: yeah
Barbara: hmmm yeah that's a mystery. cause it's not dairy but it's yoghurt xD
Haylee: exactly xD Haylee: ok i asked sb. they put it next to eggs lol
Barbara: lol"

res = summarizer(sample)
print(res)
```

### Expected Output

```
[{'summary_text': "Haylee is in the dairy section. She can't find the coconut milk yog"}]

```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 2023
- num_epochs: 5
- MAX_LENGTH_DIALOGUE = 768
- MAX_LENGTH_SUMMARY  = 128

### Model Performance

| Epoch |   Training Loss | Validation Loss |   Rouge1   |    Rouge2 |   Rougel    |   Rougelsum |
|:------|----------------:|----------------:|-----------:|----------:|------------:|------------:|
|   1   |       1.182841  |    1.202841     | 48.847000  | 25.428200 | 41.734300   |  44.999900  |
|   2   |       1.029400  |    1.217544     | 49.175000  | 25.914800 | 41.729000   |  45.258300  |
|   3   |       0.902600  |    1.239609     | 49.177600  | 25.581100 | 41.680700   |  44.997300  |
|   4   |       0.808000  |    1.274836     | 49.310200  | 25.902800 | 42.103600   |  45.485000  |
|   5   |       0.748200  |    1.304448     | 49.154700  | 25.520400 | 41.904900   |  45.234200  |
{"datasets": ["samsum"], "metrics": ["rouge"], "pipeline_tag": "summarization"}
transformers pytorch t5 text2text-generation summarization dataset:samsum autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #dataset-samsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

### Usage


### Expected Output


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 2023
* num\_epochs: 5
* MAX\_LENGTH\_DIALOGUE = 768
* MAX\_LENGTH\_SUMMARY = 128


### Model Performance




### Usage ### Expected Output ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 2023
* num\_epochs: 5
* MAX\_LENGTH\_DIALOGUE = 768
* MAX\_LENGTH\_SUMMARY = 128 ### Model Performance
TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #dataset-samsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Usage ### Expected Output ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 2023
* num\_epochs: 5
* MAX\_LENGTH\_DIALOGUE = 768
* MAX\_LENGTH\_SUMMARY = 128 ### Model Performance
TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #dataset-samsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Usage### Expected Output### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 2023
* num\_epochs: 5
* MAX\_LENGTH\_DIALOGUE = 768
* MAX\_LENGTH\_SUMMARY = 128### Model Performance