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# t5-small-billsum_model

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4109
- Rouge1: 0.2444
- Rouge2: 0.2013
- Rougel: 0.2371
- Rougelsum: 0.2372
- Gen Len: 18.9994
- Bleu: 0.0018

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu   |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|
| No log        | 1.0   | 296   | 1.8388          | 0.222  | 0.1724 | 0.2118 | 0.2119    | 18.9942 | 0.0011 |
| 2.2619        | 2.0   | 592   | 1.7177          | 0.2287 | 0.1797 | 0.2186 | 0.2187    | 18.9957 | 0.0013 |
| 2.2619        | 3.0   | 888   | 1.6596          | 0.2337 | 0.1853 | 0.2241 | 0.2242    | 18.9979 | 0.0015 |
| 1.9013        | 4.0   | 1185  | 1.6184          | 0.2359 | 0.1873 | 0.2268 | 0.2269    | 19.0    | 0.0016 |
| 1.9013        | 5.0   | 1481  | 1.5934          | 0.2372 | 0.1891 | 0.2285 | 0.2286    | 19.0    | 0.0016 |
| 1.8196        | 6.0   | 1777  | 1.5683          | 0.2378 | 0.1901 | 0.2293 | 0.2294    | 18.9985 | 0.0016 |
| 1.7679        | 7.0   | 2073  | 1.5489          | 0.2371 | 0.1901 | 0.2288 | 0.2289    | 19.0    | 0.0016 |
| 1.7679        | 8.0   | 2370  | 1.5335          | 0.2386 | 0.1924 | 0.2306 | 0.2306    | 19.0    | 0.0017 |
| 1.7315        | 9.0   | 2666  | 1.5215          | 0.239  | 0.193  | 0.2311 | 0.2312    | 19.0    | 0.0017 |
| 1.7315        | 10.0  | 2962  | 1.5094          | 0.2394 | 0.1938 | 0.2318 | 0.2317    | 19.0    | 0.0017 |
| 1.6994        | 11.0  | 3258  | 1.4996          | 0.2403 | 0.195  | 0.2325 | 0.2325    | 19.0    | 0.0017 |
| 1.6769        | 12.0  | 3555  | 1.4904          | 0.2405 | 0.1955 | 0.2328 | 0.2328    | 19.0    | 0.0017 |
| 1.6769        | 13.0  | 3851  | 1.4820          | 0.2409 | 0.1961 | 0.2333 | 0.2333    | 19.0    | 0.0017 |
| 1.659         | 14.0  | 4147  | 1.4740          | 0.2416 | 0.1971 | 0.2341 | 0.2341    | 18.9994 | 0.0017 |
| 1.659         | 15.0  | 4443  | 1.4694          | 0.242  | 0.1978 | 0.2345 | 0.2346    | 19.0    | 0.0018 |
| 1.6436        | 16.0  | 4740  | 1.4629          | 0.2425 | 0.1981 | 0.2348 | 0.2348    | 19.0    | 0.0018 |
| 1.6271        | 17.0  | 5036  | 1.4567          | 0.2428 | 0.1987 | 0.2352 | 0.2353    | 18.9994 | 0.0018 |
| 1.6271        | 18.0  | 5332  | 1.4519          | 0.243  | 0.1991 | 0.2355 | 0.2356    | 19.0    | 0.0018 |
| 1.6135        | 19.0  | 5628  | 1.4483          | 0.2429 | 0.1992 | 0.2354 | 0.2355    | 18.9994 | 0.0018 |
| 1.6135        | 20.0  | 5925  | 1.4425          | 0.2434 | 0.1998 | 0.2359 | 0.236     | 18.9994 | 0.0018 |
| 1.6038        | 21.0  | 6221  | 1.4403          | 0.2435 | 0.1999 | 0.2361 | 0.2362    | 18.9994 | 0.0018 |
| 1.5958        | 22.0  | 6517  | 1.4370          | 0.2436 | 0.2    | 0.2363 | 0.2364    | 18.9994 | 0.0018 |
| 1.5958        | 23.0  | 6813  | 1.4328          | 0.2439 | 0.2003 | 0.2366 | 0.2367    | 18.9994 | 0.0018 |
| 1.5875        | 24.0  | 7110  | 1.4308          | 0.2439 | 0.2003 | 0.2366 | 0.2367    | 18.9994 | 0.0018 |
| 1.5875        | 25.0  | 7406  | 1.4283          | 0.2439 | 0.2004 | 0.2366 | 0.2367    | 18.9994 | 0.0018 |
| 1.581         | 26.0  | 7702  | 1.4255          | 0.2438 | 0.2003 | 0.2365 | 0.2367    | 18.9994 | 0.0018 |
| 1.581         | 27.0  | 7998  | 1.4241          | 0.2438 | 0.2005 | 0.2365 | 0.2366    | 18.9994 | 0.0018 |
| 1.5734        | 28.0  | 8295  | 1.4212          | 0.244  | 0.2007 | 0.2367 | 0.2368    | 18.9994 | 0.0018 |
| 1.5697        | 29.0  | 8591  | 1.4199          | 0.244  | 0.2007 | 0.2367 | 0.2368    | 18.9994 | 0.0018 |
| 1.5697        | 30.0  | 8887  | 1.4173          | 0.244  | 0.2007 | 0.2368 | 0.2368    | 18.9994 | 0.0018 |
| 1.5639        | 31.0  | 9183  | 1.4168          | 0.2439 | 0.2007 | 0.2367 | 0.2368    | 18.9994 | 0.0018 |
| 1.5639        | 32.0  | 9480  | 1.4159          | 0.2441 | 0.2007 | 0.2367 | 0.2368    | 18.9994 | 0.0018 |
| 1.5608        | 33.0  | 9776  | 1.4143          | 0.2442 | 0.2009 | 0.2369 | 0.237     | 18.9994 | 0.0018 |
| 1.5562        | 34.0  | 10072 | 1.4132          | 0.2442 | 0.2009 | 0.2369 | 0.237     | 18.9994 | 0.0018 |
| 1.5562        | 35.0  | 10368 | 1.4123          | 0.2442 | 0.201  | 0.2369 | 0.237     | 18.9994 | 0.0018 |
| 1.5563        | 36.0  | 10665 | 1.4122          | 0.2443 | 0.2012 | 0.237  | 0.2371    | 18.9994 | 0.0018 |
| 1.5563        | 37.0  | 10961 | 1.4112          | 0.2443 | 0.2011 | 0.237  | 0.2371    | 18.9994 | 0.0018 |
| 1.5526        | 38.0  | 11257 | 1.4112          | 0.2444 | 0.2013 | 0.2371 | 0.2373    | 18.9994 | 0.0018 |
| 1.5525        | 39.0  | 11553 | 1.4110          | 0.2443 | 0.2012 | 0.237  | 0.2372    | 18.9994 | 0.0018 |
| 1.5525        | 39.97 | 11840 | 1.4109          | 0.2444 | 0.2013 | 0.2371 | 0.2372    | 18.9994 | 0.0018 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["billsum"], "metrics": ["rouge", "bleu"], "model-index": [{"name": "t5-small-billsum_model", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "billsum", "type": "billsum", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "rouge", "value": 0.2444, "name": "Rouge1"}, {"type": "bleu", "value": 0.0018, "name": "Bleu"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:billsum 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-billsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-billsum\_model
=======================


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


* Loss: 1.4109
* Rouge1: 0.2444
* Rouge2: 0.2013
* Rougel: 0.2371
* Rougelsum: 0.2372
* Gen Len: 18.9994
* Bleu: 0.0018


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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-billsum #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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-billsum #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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3