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# flan-t5-large-samsum

This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the samsum dataset.

It achieves the following results on the evaluation set:
- Loss: 1.1754
- Rouge1: 54.1595
- Rouge2: 29.1081
- Rougel: 45.4989
- Rougelsum: 49.1026
- Gen Len: 28.72

> Note: the stacked version of this model technically does evaluation on a **different** validation set (the stacked one) while this just uses `samsum`.
## Model description

More information needed

## Intended uses & limitations

- Intended for comparison(s) to the [stacked version of this model](https://huggingface.co/stacked-summaries/flan-t5-large-stacked-samsum-1024)
- 1024 token input max

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 17868
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.04
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.2106        | 0.43  | 50   | 1.1889          | 52.5898 | 26.9967 | 43.6944 | 47.9656   | 24.5167 |
| 1.213         | 0.87  | 100  | 1.1760          | 52.4279 | 27.4689 | 43.7873 | 48.0581   | 25.0533 |
| 1.0726        | 1.3   | 150  | 1.1731          | 52.8246 | 26.9524 | 43.7429 | 48.0345   | 25.55   |
| 1.0784        | 1.74  | 200  | 1.1708          | 53.1291 | 27.9056 | 44.2609 | 48.6883   | 26.03   |
| 1.0215        | 2.17  | 250  | 1.1755          | 53.1512 | 27.9475 | 44.1442 | 48.4619   | 27.57   |
| 1.0294        | 2.61  | 300  | 1.1711          | 53.4402 | 28.0126 | 44.5454 | 48.6432   | 25.9033 |
| 1.0016        | 3.04  | 350  | 1.1718          | 53.9395 | 28.3087 | 45.191  | 49.2773   | 26.6133 |
| 0.9576        | 3.48  | 400  | 1.1741          | 53.9004 | 28.3243 | 45.0911 | 48.9182   | 26.33   |
| 0.9739        | 3.91  | 450  | 1.1754          | 53.7049 | 28.419  | 44.8946 | 48.8708   | 27.2433 |
| 0.9505        | 4.35  | 500  | 1.1781          | 53.7142 | 28.1758 | 44.8324 | 48.9498   | 26.8667 |
| 0.9993        | 4.78  | 550  | 1.1784          | 53.87   | 28.2211 | 44.893  | 49.1074   | 27.2167 |

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["samsum"], "metrics": ["rouge"], "pipeline_tag": "summarization", "base_model": "google/flan-t5-large", "model-index": [{"name": "stacked-summaries/flan-t5-large-samsum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "samsum", "type": "samsum", "config": "samsum", "split": "test"}, "metrics": [{"type": "rouge", "value": 49.0095, "name": "ROUGE-1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGNhY2RhOTg5ZmU4ZGJiMjI1NjUyYWMwYmM2Mzk4MGEwMjk0NDg2OWYxZDdmM2I4NzBmODNiM2JmNTg1MDJhYSIsInZlcnNpb24iOjF9.YinJDLeqzoU_x5uJbGIgq8ZEs36oC3Pzre_vk2juxngBoXCEw54XWjpvVhKKZXeIgc47otucJFtFwAOPEmt9Bw"}, {"type": "rouge", "value": 25.681, "name": "ROUGE-2", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDBmNDc4NGMzZGEzYzMzMTFiNzliNjUyYmY0MzNjMmRlMTk4ZTRmZmUxODE0MmY1MjEzOWQ2MGQxMmZmZmQ5MSIsInZlcnNpb24iOjF9.UmRHCmQR5CR-JklBTY1JnjD_Gqz_qMYwdVXhMMvnAynMwAgXkoJZeoxT--usUfdkbqaQ-mLeEvLw7mgNE-NQAw"}, {"type": "rouge", "value": 41.4474, "name": "ROUGE-L", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODdiM2IxZTU4NTEyMzlmZDEzYTliZWNjMjM1NTAzMjE5MDY1MDZiZDc2YmE2NzUxOWJhMmQ0NTM5MjRjZjQyMSIsInZlcnNpb24iOjF9.PeJ41sirLWf3HTiJXlSMNoleENJT_X2u4VMkgQTmXMmGkbrONTFbUYwO4qjoQkvyjy8pLA2eQ3Fjm5yAvKrTCQ"}, {"type": "rouge", "value": 45.1556, "name": "ROUGE-LSUM", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFiMGNkZDYxZmVhMDFlNDRlNmQ4YWVlMTk3ODI0ZWQ2MmIzNWFkYjkwOWRlNzkyNGVmYmY5ZTczZDAxYTk3NiIsInZlcnNpb24iOjF9.dsicHh5W4ba8t8eBBcSRUm-HLPlMoRc57XixiOHBCk-82De5u8hH8fsRWbMmaLpobdJ7b3xlIaVfTfMMRoLvBw"}, {"type": "loss", "value": 1.2201015949249268, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzliODE2YzY2MzMyZDQ4YzdjZmFjNTc2NDU3ZjQwNjYwNTdhZjY1NWViM2VhNDc1MjQzNDkxMDI2MTM5ZjFkYiIsInZlcnNpb24iOjF9.2QdP4Zj2oHCo0HCoGgZy6YdqNJaQ0ri0E2kD7lzYbVmyg35wyGutvRUaXVR6O833gTbsCvM86Gp77qNT9CTyDA"}, {"type": "gen_len", "value": 17.326, "name": "gen_len", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWJlNjhjMmUxNWU3MDZlMTUzOWRmM2UwNmU3MjBjODhmMGYxZTUyMmFmMmE0MmU3ZTVkYWY0MDhkMWQ3NTk2MSIsInZlcnNpb24iOjF9.wFaw7DOpESjPu_uW6liHc4XaTwF36ReLLYd-BBFhnZXemE_lGQxmp0O0Vl2DgZz3SSbXonyS4D01G2hYze8qCA"}]}]}]}
transformers pytorch safetensors t5 text2text-generation generated_from_trainer summarization en dataset:samsum base_model:google/flan-t5-large license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #generated_from_trainer #summarization #en #dataset-samsum #base_model-google/flan-t5-large #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

flan-t5-large-samsum
====================


This model is a fine-tuned version of google/flan-t5-large on the samsum dataset.


It achieves the following results on the evaluation set:


* Loss: 1.1754
* Rouge1: 54.1595
* Rouge2: 29.1081
* Rougel: 45.4989
* Rougelsum: 49.1026
* Gen Len: 28.72



> 
> Note: the stacked version of this model technically does evaluation on a different validation set (the stacked one) while this just uses 'samsum'.
> 
> 
> 


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


* Intended for comparison(s) to the stacked version of this model
* 1024 token input max


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 17868
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.04
* num\_epochs: 5.0


### Training results




### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 17868
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.04
* num\_epochs: 5.0 ### Training results
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #generated_from_trainer #summarization #en #dataset-samsum #base_model-google/flan-t5-large #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 17868
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.04
* num\_epochs: 5.0 ### Training results
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #generated_from_trainer #summarization #en #dataset-samsum #base_model-google/flan-t5-large #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 17868
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.04
* num\_epochs: 5.0### Training results