transformers pytorch tensorboard t5 text2text-generation summarization dataset:billsum license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- billsum
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- t5
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’s 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’s 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’s license or state identification
    card. (5) For each electronic affidavit, the Secretary of State shall obtain an
    electronic copy of the applicant’s signature from his or her driver’s 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’s 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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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
