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# t5-small-finetuned-billsum-ca_test

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: 2.3376
- Rouge1: 12.6315
- Rouge2: 6.9839
- Rougel: 10.9983
- Rougelsum: 11.9383
- Gen Len: 19.0

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 495  | 2.4805          | 9.9389  | 4.1239 | 8.3979  | 9.1599    | 19.0    |
| 3.1564        | 2.0   | 990  | 2.3833          | 12.1026 | 6.5196 | 10.5123 | 11.4527   | 19.0    |
| 2.66          | 3.0   | 1485 | 2.3496          | 12.5389 | 6.8686 | 10.8798 | 11.8636   | 19.0    |
| 2.5671        | 4.0   | 1980 | 2.3376          | 12.6315 | 6.9839 | 10.9983 | 11.9383   | 19.0    |


### Framework versions

- Transformers 4.12.2
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["summarization", "t5"], "datasets": ["billsum"], "metrics": ["rouge"], "widget": [{"text": "The people of the State of California do enact as follows: SECTION 1. The Legislature hereby finds and declares as follows: (a) Many areas of the state are disproportionately impacted by drought because they are heavily dependent or completely reliant on groundwater from basins that are in overdraft and in which the water table declines year after year or from basins that are contaminated. (b) There are a number of state grant and loan programs that provide financial assistance to communities to address drinking water and wastewater needs. Unfortunately, there is no program in place to provide similar assistance to individual homeowners who are reliant on their own groundwater wells and who may not be able to afford conventional private loans to undertake vital water supply, water quality, and wastewater improvements. (c) The program created by this act is intended to bridge that gap by providing low-interest loans, grants, or both, to individual homeowners to undertake actions necessary to provide safer, cleaner, and more reliable drinking water and wastewater treatment. These actions may include, but are not limited to, digging deeper wells, improving existing wells and related equipment, addressing drinking water contaminants in the homeowner\u2019s water, or connecting to a local water or wastewater system. SEC. 2. Chapter 6.6 (commencing with Section 13486) is added to Division 7 of the Water Code, to read: CHAPTER  6.6. Water and Wastewater Loan and Grant Program 13486. (a) The board shall establish a program in accordance with this chapter to provide low-interest loans and grants to local agencies for low-interest loans and grants to eligible applicants for any of the following purposes:", "example_title": "Water use"}, {"text": "The people of the State of California do enact as follows:   SECTION 1. Section 2196 of the Elections Code is amended to read: 2196. (a) (1) Notwithstanding any other provision of law, a person who is qualified to register to vote and who has a valid California driver\u2019s license or state identification card may submit an affidavit of voter registration electronically on the Internet Web site of the Secretary of State. (2) An affidavit submitted pursuant to this section is effective upon receipt of the affidavit by the Secretary of State if the affidavit is received on or before the last day to register for an election to be held in the precinct of the person submitting the affidavit. (3) The affiant shall affirmatively attest to the truth of the information provided in the affidavit. (4) For voter registration purposes, the applicant shall affirmatively assent to the use of his or her signature from his or her driver\u2019s license or state identification card. (5) For each electronic affidavit, the Secretary of State shall obtain an electronic copy of the applicant\u2019s signature from his or her driver\u2019s license or state identification card directly from the Department of Motor Vehicles. (6) The Secretary of State shall require a person who submits an affidavit pursuant to this section to submit all of the following: (A) The number from his or her California driver\u2019s license or state identification card. (B) His or her date of birth. (C) The last four digits of his or her social security number. (D) Any other information the Secretary of State deems necessary to establish the identity of the affiant. (7) Upon submission of an affidavit pursuant to this section, the electronic voter registration system shall provide for immediate verification of both of the following:", "example_title": "Election"}], "model-index": [{"name": "t5-small-finetuned-billsum-ca_test", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "billsum", "type": "billsum", "args": "default"}, "metrics": [{"type": "rouge", "value": 12.6315, "name": "Rouge1"}]}, {"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "billsum", "type": "billsum", "config": "default", "split": "test"}, "metrics": [{"type": "rouge", "value": 12.1368, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 4.6017, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 10.0767, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 10.6892, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 2.897707462310791, "name": "loss", "verified": true}, {"type": "gen_len", "value": 19.0, "name": "gen_len", "verified": true}]}]}]}
transformers pytorch tensorboard t5 text2text-generation summarization dataset:billsum license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #dataset-billsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-finetuned-billsum-ca\_test
===================================


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


* Loss: 2.3376
* Rouge1: 12.6315
* Rouge2: 6.9839
* Rougel: 10.9983
* Rougelsum: 11.9383
* Gen Len: 19.0


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.12.2
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.12.2
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.2
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.12.2
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3