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# t5-small-finetuned-cnndm_3epoch_v2

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6070
- Rouge1: 24.7696
- Rouge2: 11.9467
- Rougel: 20.4495
- Rougelsum: 23.3341
- Gen Len: 18.9999

## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9695        | 0.07  | 5000   | 1.7781          | 24.2253 | 11.472  | 20.0367 | 22.8469   | 18.9962 |
| 1.9536        | 0.14  | 10000  | 1.7575          | 24.2983 | 11.469  | 20.0054 | 22.9144   | 18.9995 |
| 1.9452        | 0.21  | 15000  | 1.7406          | 24.2068 | 11.4601 | 20.0021 | 22.8375   | 19.0    |
| 1.931         | 0.28  | 20000  | 1.7302          | 24.1589 | 11.4183 | 19.9736 | 22.7804   | 18.9996 |
| 1.9182        | 0.35  | 25000  | 1.7381          | 24.1634 | 11.5435 | 19.9643 | 22.7371   | 18.9999 |
| 1.9072        | 0.42  | 30000  | 1.7239          | 24.4401 | 11.6323 | 20.1243 | 22.9468   | 19.0    |
| 1.9027        | 0.49  | 35000  | 1.7162          | 24.1801 | 11.4788 | 20.0011 | 22.832    | 18.9996 |
| 1.8962        | 0.56  | 40000  | 1.7060          | 24.4153 | 11.6275 | 20.1742 | 23.0865   | 18.9998 |
| 1.8905        | 0.63  | 45000  | 1.7004          | 24.1446 | 11.5402 | 19.9986 | 22.7949   | 18.9983 |
| 1.8764        | 0.7   | 50000  | 1.6876          | 24.342  | 11.5448 | 20.0993 | 22.9509   | 18.9993 |
| 1.8772        | 0.77  | 55000  | 1.6879          | 24.3596 | 11.6063 | 20.1592 | 22.9966   | 19.0    |
| 1.8669        | 0.84  | 60000  | 1.6776          | 24.6201 | 11.6668 | 20.2639 | 23.201    | 18.9994 |
| 1.8692        | 0.91  | 65000  | 1.6838          | 24.2924 | 11.6129 | 20.1071 | 22.9112   | 18.9997 |
| 1.8552        | 0.98  | 70000  | 1.6885          | 24.2878 | 11.6773 | 20.1272 | 22.8797   | 18.9992 |
| 1.8205        | 1.04  | 75000  | 1.6717          | 24.5579 | 11.6421 | 20.2593 | 23.1442   | 19.0    |
| 1.8074        | 1.11  | 80000  | 1.6604          | 24.495  | 11.6542 | 20.1854 | 23.1091   | 18.9996 |
| 1.7951        | 1.18  | 85000  | 1.6705          | 24.4504 | 11.6601 | 20.2185 | 23.0597   | 18.9999 |
| 1.7937        | 1.25  | 90000  | 1.6645          | 24.5535 | 11.6921 | 20.2087 | 23.1099   | 18.9999 |
| 1.8017        | 1.32  | 95000  | 1.6647          | 24.5179 | 11.8005 | 20.2903 | 23.13     | 18.9993 |
| 1.7918        | 1.39  | 100000 | 1.6568          | 24.518  | 11.7528 | 20.222  | 23.0767   | 18.9991 |
| 1.7985        | 1.46  | 105000 | 1.6588          | 24.4636 | 11.636  | 20.1038 | 23.032    | 19.0    |
| 1.7944        | 1.53  | 110000 | 1.6498          | 24.6611 | 11.78   | 20.3059 | 23.2404   | 18.9999 |
| 1.7934        | 1.6   | 115000 | 1.6551          | 24.7267 | 11.823  | 20.3377 | 23.273    | 18.9997 |
| 1.7764        | 1.67  | 120000 | 1.6467          | 24.5052 | 11.8052 | 20.2617 | 23.1228   | 18.9996 |
| 1.7846        | 1.74  | 125000 | 1.6489          | 24.5423 | 11.8407 | 20.3464 | 23.1433   | 18.9999 |
| 1.7799        | 1.81  | 130000 | 1.6438          | 24.4915 | 11.7827 | 20.2592 | 23.1299   | 18.9999 |
| 1.7806        | 1.88  | 135000 | 1.6353          | 24.7804 | 11.9212 | 20.4678 | 23.359    | 19.0    |
| 1.7784        | 1.95  | 140000 | 1.6338          | 24.7892 | 11.8836 | 20.4227 | 23.373    | 18.9997 |
| 1.7551        | 2.02  | 145000 | 1.6341          | 24.6828 | 11.8257 | 20.3862 | 23.2536   | 18.9997 |
| 1.728         | 2.09  | 150000 | 1.6328          | 24.6697 | 11.851  | 20.3943 | 23.2738   | 18.9993 |
| 1.7201        | 2.16  | 155000 | 1.6309          | 24.7364 | 11.8505 | 20.365  | 23.2885   | 18.9992 |
| 1.7233        | 2.23  | 160000 | 1.6346          | 24.7298 | 12.0026 | 20.4444 | 23.3156   | 18.9999 |
| 1.7096        | 2.3   | 165000 | 1.6253          | 24.6443 | 11.9004 | 20.4138 | 23.2583   | 18.9999 |
| 1.7084        | 2.37  | 170000 | 1.6233          | 24.6688 | 11.8885 | 20.3623 | 23.2608   | 18.9996 |
| 1.7236        | 2.44  | 175000 | 1.6243          | 24.7174 | 11.8924 | 20.4012 | 23.2948   | 18.9996 |
| 1.7108        | 2.51  | 180000 | 1.6188          | 24.6013 | 11.8153 | 20.2969 | 23.1867   | 18.9997 |
| 1.711         | 2.58  | 185000 | 1.6125          | 24.7673 | 11.8646 | 20.3805 | 23.3114   | 18.9997 |
| 1.7108        | 2.65  | 190000 | 1.6101          | 24.8047 | 11.9763 | 20.494  | 23.3873   | 18.9998 |
| 1.7114        | 2.72  | 195000 | 1.6123          | 24.7019 | 11.9201 | 20.414  | 23.2823   | 18.9999 |
| 1.7004        | 2.79  | 200000 | 1.6083          | 24.7525 | 11.9197 | 20.4581 | 23.3371   | 18.9999 |
| 1.7104        | 2.86  | 205000 | 1.6061          | 24.7057 | 11.8818 | 20.4017 | 23.286    | 18.9999 |
| 1.7063        | 2.93  | 210000 | 1.6063          | 24.7707 | 11.934  | 20.4473 | 23.3316   | 18.9999 |
| 1.7039        | 3.0   | 215000 | 1.6070          | 24.7696 | 11.9467 | 20.4495 | 23.3341   | 18.9999 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm_3epoch_v2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailymail", "args": "3.0.0"}, "metrics": [{"type": "rouge", "value": 24.7696, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:cnn_dailymail license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-finetuned-cnndm\_3epoch\_v2
====================================


This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6070
* Rouge1: 24.7696
* Rouge2: 11.9467
* Rougel: 20.4495
* Rougelsum: 23.3341
* Gen Len: 18.9999


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: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #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: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #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: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6