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setfit-hub-multilabel-example

Text Classification
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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- Kevinger/hub-report-dataset
metrics:
- accuracy
widget:
- text: 'A 16-acre property once home to the long-shuttered Foxborough State Hospital
will soon provide housing for 141 low-income senior households.
Walnut Street, an affordable housing project being developed by the Affordable
Housing Services Collaborative and Onyx, will turn land that has been vacant for
decades into much-needed affordable housing.
“Housing is empowering. No matter our age, it is a comfort not to worry about
whether we can afford a place,” Onyx CEO Chanda Smart said at a press conference
Thursday. “Senior housing for the town of Foxborough means that seniors who worked
and raised their families here in Foxborough still have the opportunity to remain
here.”
Foxborough State Hospital opened in 1889 as the Massachusetts Hospital for Dipsomaniacs
and Inebriates for treatment of alcoholism, according to the National Park Service,
and was later converted to a standard psychiatric hospital. It closed in 1975,
and parts of the property have already been redeveloped over the years.
The Foxborough Housing Authority first began working on the project back in 2011.
The land was transferred to the agency from the state in 2017 to be used for affordable
housing.
Acting Town Manager Paige Duncan told MassLive that the town held a number of
community meetings to decide what to build on the property.
“It was controversial, but what came out was a clear support for senior housing,”
she said. “We really tried to address the needs of the community and we came up
with a project that was sensitive to the area. We didn’t want a big block of buildings
that towered over the neighborhood.”
After that, she said, there was overwhelming support for the project. The permits
were filed in February and approved by April, an almost unheard-of timeline.
The finished project will provide 141 new apartments for residents age 55 and
over. Of those, 35 will be reserved for people making 30% or less of the area
median income, and 85 will be for those making 60% AMI. Foxborough residents will
be given preference for 70% of the units.
A second phase of the project once this one is complete will add approximately
60 more units.
Greg Spiers, chairman of the Housing Authority, said the new senior housing was
badly needed, noting there are about 5,500 elderly and disabled people on public
housing waiting lists in Massachusetts.
“With 195 of those on that list Foxborough residents, that 70% local preference
for first-time rentals is one of our goals,” he said. “The need is so great for
affordable housing in our area and the entire state.”
Housing and Livable Communities Secretary Ed Augustus praised the town for its
dedication to creating more affordable housing, even though more than 10% of its
total housing units qualify as affordable. The 10% threshold is the state requirement
to stop projects being filed under Chapter 40B, a law which allows affordable
housing developments to bypass certain local permitting requirements.
“You know that that is just an arbitrary number, but the real needs are significantly
more than that,” Augustus said. “We need more communities to take note of what
Foxborough is doing.”
Lt. Gov. Kim Driscoll said the project is a good example of the use of surplus
state land for housing. Gov. Maura Healey’s housing bond bill filed in October
included a proposed $30 million that would support similar projects to use underutilized
state property for housing. Healey also issued an executive order requesting state
agencies to conduct an audit of their property to find land any surplus land suitable
for this purpose.
“Converting state-owned land to another entity can be a little bit of a torturous
pathway. We know that building all the resources you need takes time,” Driscoll
said Thursday. “How do we leverage the cost of land, which is one of the reasons
housing is so expensive, to build the type of housing we need, but do it in a
shorter timetable? That’s what this (project) is all about.”
The project has received more than $25 million in state and federal funding, including
through American Rescue Plan Act rental funds and state and federal Low Income
Housing Tax Credits. Work on the site has not yet started.'
- text: '“I was on my co-op last year for, like, a straight year, so coming back to
campus feels kind of nerve-wracking,” said Jasmine Rodriguez, 21. “But I feel
more experienced than I did in my first year. I had a lot of anxiety in my first
year, but now it’s been really chill.”
As about a dozen Northeastern University students went around a conference table
talking about their college experiences, voices were soft and answers halting,
at least initially. Gradually, though, the students at this check-in meeting last
fall began to open up and speak candidly about the challenges and adjustments
of college life.
Advertisement
The students were Black, Latino, and Asian American and ranged from first-years
to seniors, mostly from neighborhoods across Boston; the majority were the first
generation of their family to attend college. Most were their high schools’ valedictorians
— hardworking, smart students who excelled despite lacking the advantages of many
peers.
That’s where The Valedictorian Project came in.
The Boston-based nonprofit was founded in 2020 in response to the Boston Globe’s
award-winning 2019 investigative series, The Valedictorians Project, which found
that the city’s best and brightest public school students often encounter major
obstacles to their academic and professional goals. (The Globe is not involved
with the organization.)
The Valedictorian Project matches participating high school graduates with peer
mentors close to their age and a senior mentor who is an experienced professional
in their intended line of work. It also provides a $500 stipend for books and
other necessities, and supplemental support through partnerships with other organizations
to help students navigate their new lives on campus and choose career paths.
“Many of our mentors are first-gen college students themselves,” cofounder and
executive director Amy McDermott said in an interview. “Many navigated very similar
personal backgrounds to our mentees. I hear often in our mentor interviews, they
want to be that person that they wish they had in navigating college.”
Advertisement
This academic year marks a milestone for the organization, as its first cohort
of college freshmen are now seniors.
McDermott said the organization began by inviting Boston valedictorians to participate
in its first year, then added students from Lawrence in year two, Brockton and
Worcester in 2022, and Chelsea last spring.
Jasmine Rodriguez took part in a roundtable discussion at Northeastern University
for students participating in The Valedictorian Project. Jonathan Wiggs/Globe
Staff
Mentor John Marley, 30, of Taunton, said the organization helps level the playing
field for young people who don’t come from privileged backgrounds.
“Students from wealthier families have always had these mentorship relationships,
always had these connections, and those things are just unseen,” said Marley,
an attorney whose family came to the United States from Jamaica when he was 5.
“Unfairly or not ... it’s always advantaged a particular group and class of students
over another. And I think they do a good job addressing that.”
This academic year, The Valedictorian Project is supporting 140 students, of whom
about three-quarters are first-generation college students and roughly 85 percent
are people of color, according to McDermott. Besides Northeastern, students in
the program attend Boston University, Harvard, MIT, Tufts, Brown, Yale, Stanford,
and other colleges around the country, she said.
As a student of color at an expensive private university, Rodriguez said, “You
have to physically go out and try to find people that look like you. And I feel
like for everyone else, it’s very easy. They find them in their classes. But it’s
like, in my classes there’ll be like one other Black or Hispanic person.”
Advertisement
Rodriguez, a Dorchester native majoring in communications and sociology, recently
spent a year as a social media co-op for an organization that supports domestic
violence victims. She is drawn to work that will help others, she said, because
she saw people in need in her neighborhood and her own family as she grew up.
“I saw a lot of people that look like me struggle and go through a lot of things,”
she said. “My mom is an immigrant. … We grew up on Section 8 [housing assistance];
we grew up on food stamps and stuff like that.”
Ciana Omnis participated in a Northeastern University roundtable discussion for
students participating in The Valedictorian Project. Jonathan Wiggs/Globe Staff
Ciana Omnis, 20, a third-year industrial engineering major who grew up in Florida,
moved to Dorchester at age 14, and was the 2021 valedictorian at Brighton High
School. She is the eldest of three children, so she can’t lean on older siblings
for advice, she said.
Her father, a truck driver who immigrated to the United States from Haiti, didn’t
complete high school, she said, while her mother, a health care administrator,
completed an associate’s degree but doesn’t yet have her bachelor’s.
“I’ve met a lot of people in college who have parents who have done four-year
degrees or whatnot, or even other kinds of higher education, so they’re able to
get advice from their parents,” Omnis said. “For me, it’s been a bit harder, because
I have to kind of figure out certain things on my own.”
Advertisement
Her mentors help fill that gap, she said, and the program helps her “meet other
people who have the same background as me.”
After they met through a Valedictorian Project event, John Le, who was the 2022
valedictorian at East Boston High School, became friends with Connor Lashley,
the 2022 valedictorian at Jeremiah E. Burke High School in Dorchester.
“One of the issues is socializing, like making a friend group, because from my
experience, from each class you kind of like meet people there, but if you’re
not in the same major, you might not be able to maintain a relationship with them,”
said Le, 20.
The Valedictorian Project, he added, “has really been helpful to meet people at
Northeastern and ... find people with similar interests.”
Lashley, 19, said his mentors have helped him learn how to network with others
in his field and steered him toward scholarship opportunities, and he can count
on their support whenever he needs it.
“They’re pretty much available the same day if stuff comes up,” he said.
Connor Lashley (left) and John Le took part in a roundtable discussion at Northeastern
University for students participating in The Valedictorian Project. Jonathan Wiggs/Globe
Staff
Jeremy C. Fox can be reached at jeremy.fox@globe.com. Follow him @jeremycfox.'
- text: 'LEVERETT — Dakin Humane Society announced Wednesday that it has sold its
former animal shelter at 63 Montague Road in Leverett to Better Together Dog Rescue.
The news release didn’t include a sales price for the 3,480-square-foot building
on 5 acres of land.
But records at the Franklin County Registry of Deeds show the sale was for $575,000.'
- text: 'Joan Acocella, a cultural critic whose elegant, erudite essays about dance
and literature appeared in The New Yorker and The New York Review of Books for
more than four decades, died on Sunday at her home in Manhattan. She was 78.
Her son, Bartholomew Acocella, said the cause was cancer.
Ms. Acocella (pronounced ack-ah-CHELL-uh) wrote deeply about dancers and choreographers,
including Mikhail Baryshnikov, Suzanne Farrell and George Balanchine. She scrutinized
the vicissitudes of the New York City Ballet as well as the feats of the ballroom-dancing
pros and celebrity oafs of the popular TV series “Dancing With the Stars.”
She was The New Yorker’s dance critic from 1998 to 2019 and freelanced for The
Review for 33 years. Her final articles for The Review were a two-part commentary
in May on the biography “Mr. B: George Balanchine’s 20th Century,” by Jennifer
Homans, her successor as The New Yorker’s dance critic.
“What she wrote for us,” Emily Greenhouse, the editor of The Review, said in an
email, “was often mischievous and always delicious — on crotch shots and cuss
words, on Neapolitan hand gestures and Isadora Duncan’s emphasis on the solar
plexus.”'
- text: 'StreetsblogMASS relies on the generous support of readers like you. Help
us meet our year-end fundraising goals – give today!
Last week, the labor union that represents most Boston police officers ratified
a new contract that will introduce a number of reforms – including one that will
start allowing civilians to take unwanted traffic detail shifts at construction
sites.
Under the former contract, Boston Police officers were the only people allowed
to direct traffic for events and at construction sites. And they got paid extremely
handsomely to do so: Boston police working as flaggers take home $60 an hour.
In spite of that lucrative pay, Boston has a lot of construction sites, and fewer
and fewer people who want to wear a police uniform.
Boston City Councilor Kendra Lara told StreetsblogMASS earlier this year that
over 40 percent of requests for police details at construction sites were going
unfilled.
The new labor contract removes a key barrier to reforming this system. But there
is still a city ordinance on the books that requires at least one Boston Police
officer at every city construction site "to protect the safety and general welfare
of the public and to preserve the free circulation of traffic."
A press spokesperson for Mayor Michelle Wu told StreetsblogMASS last week that
their office is aware of the ordinance and has "identified multiple legal paths
to implementing the new collective bargaining agreement."
Old rules created absurd delays for street projects
Councilor Lara also told StreetsblogMASS that many privately-run construction
sites will simply ignore the law and do their work without a flagger if nobody
responds to their requests for a detail.
But construction firms who are sticklers for the rules can end up waiting months
before a cop shows up to let them get their work done.
That''s what happened earlier this year in Oak Square, where the MBTA waited a
full year for a police detail to show up so that they could paint some new crosswalks
on Washington Street in Oak Square.
Neighbors report that those crosswalks finally got painted in August – after a
year-long wait.
New contract hikes pay, allows civilian flaggers
For all these reasons, allowing civilian flaggers at construction sites had been
one of the city''s key points of negotiation for a new collective bargaining agreement
with its police union.
Police details will still be required at "high-priority" events and construction
sites, which involve major streets, busy intersections, or major events that anticipate
over 5,000 attendees. The new contract would also pay cops who work those high-priority
details "the highest overtime rate of the most senior officer."
At other worksites, such as those along quiet neighborhood streets, Boston Police
would still get the right of first refusal to fill traffic details. But if no
Boston Police are interested, the work can be offered to other non-BPD certified
officers, including campus police and retired Boston cops. If people with those
qualifications still aren''t interested, construction contractors can then offer
the job to civilian workers.
The agreement further specifies that anyone directing traffic in those lower-priority
sites will earn $60 per hour.
The new agreement will also ban cops from double-booking their shifts, which allowed
some to get paid twice for the same period of time when one detail ended early.
Incredibly, the police department is still using a labor-intensive paper-based
system to assign details in each police district. The new agreement will allow
for a citywide electronic scheduling system.'
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Kevinger/hub-report-dataset
type: Kevinger/hub-report-dataset
split: test
metrics:
- type: accuracy
value: 0.6529242569511026
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Kevinger/hub-report-dataset](https://huggingface.co/datasets/Kevinger/hub-report-dataset) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [Kevinger/hub-report-dataset](https://huggingface.co/datasets/Kevinger/hub-report-dataset)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.6529 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Kevinger/setfit-hub-multilabel-example")
# Run inference
preds = model("LEVERETT — Dakin Humane Society announced Wednesday that it has sold its former animal shelter at 63 Montague Road in Leverett to Better Together Dog Rescue.
The news release didn’t include a sales price for the 3,480-square-foot building on 5 acres of land.
But records at the Franklin County Registry of Deeds show the sale was for $575,000.")
```
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*List how someone could finetune this model on their own dataset.*
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 53 | 386.3906 | 2161 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 75
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1 | 0.1304 | - |
| 0.0417 | 50 | 0.1596 | - |
| 0.0833 | 100 | 0.132 | - |
| 0.125 | 150 | 0.0064 | - |
| 0.1667 | 200 | 0.0017 | - |
| 0.2083 | 250 | 0.0004 | - |
| 0.25 | 300 | 0.0001 | - |
| 0.2917 | 350 | 0.0002 | - |
| 0.3333 | 400 | 0.0003 | - |
| 0.375 | 450 | 0.0002 | - |
| 0.4167 | 500 | 0.0001 | - |
| 0.4583 | 550 | 0.0002 | - |
| 0.5 | 600 | 0.0002 | - |
| 0.5417 | 650 | 0.0002 | - |
| 0.5833 | 700 | 0.0001 | - |
| 0.625 | 750 | 0.0001 | - |
| 0.6667 | 800 | 0.0001 | - |
| 0.7083 | 850 | 0.0001 | - |
| 0.75 | 900 | 0.0 | - |
| 0.7917 | 950 | 0.0001 | - |
| 0.8333 | 1000 | 0.0001 | - |
| 0.875 | 1050 | 0.0001 | - |
| 0.9167 | 1100 | 0.0001 | - |
| 0.9583 | 1150 | 0.0 | - |
| 1.0 | 1200 | 0.0001 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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