README.md

metadata

base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >
      I noticed something missing in Gail's and Bret's banter about the
      debt-ceiling vote that is typical republican mush!Bret gets Gail to agree
      that spending is too high, then Bret proceeds to suggest it's time to
      raise the retirement age for Social Security! And then...wait for
      it......Bret mentions nothing about raising taxes on corporations and
      billionaires!Bret, you would agree that the quaint 1950s was a time of
      sanity in the GOP. ....Well, in those good ol' days, top marginal tax
      rates were in the 70% range.....What's more, our national debt was low,
      like around zero!?....And what's even more, the USA was absolutely first
      in the world in reading and math scores.Enough.
  - text: >
      Denial is not limited to American politicians. It seems China is extreme
      in this category. All the 'Zero Covid' policy did was delay the
      inevitable. China is the US under Trump. Using vaccines which, while home
      grown, are not as effective only placed its population a great risk. They
      will have the same strain on their healthcare system. Very Sad.
  - text: >
      China knows everything about its citizens, monitors every details in their
      lives but somehow can't say how many people exactly died from Covid19
      since it ended its zero covid policy.Why should we believe these numbers
      instead of last week numbers?
  - text: >
      Johnny  G These figures are also not accurate or believable. Crematoria in
      China's large cities have been overrun with bodies since the zero-covid
      policy ended--running at full capacity with long backlogs. Any back of the
      envelope calculation would give a much higher death figure than
      60,000--and the virus hasn't even ravaged the countryside yet. That will
      happen over the next 3-4 weeks as migrant workers and others return to
      their villages to celebrate the Chinese New Year on Jan. 21. Due to the
      backwardness of rural healthcare and the proportionally high concentration
      of elderly people in the countryside, the covid death toll in rural China
      within the next few weeks will be high but will also receive much less
      media attention.
  - text: >
      I was beaten and verbally abused until age 17, when I could escape my
      home.  My family "looked" normal from the outside, but was not.  Child
      abuse was not yet in the lexicon.I turned out normal!  This I owe to
      visiting lots of friends and watching how their families interacted--they
      were kind.  I asked their parents to adopt me. I watched family
      sitcoms--the opposite of my homelife.  I did well in school, so I received
      praise there, and made friends.The folks wanted me to marry well and have
      kids.  But the Zero Population Movement, and Women's Lib, gave me a window
      into how humans harm the planet, and that women could do more than have
      babies and do laundry.  I put myself through uni, had no children, and
      have had and have careers I love.Parenting is the most important, unpaid
      job one can take on because it demands selflessly developing a decent,
      caring, intellectually curious, kind, patient human.  People lacking these
      qualities should re-think parenthood.Also, consider the childless life, to
      save the planet.
inference: true
model-index:
  - name: SetFit with sentence-transformers/all-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy

SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression 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 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/all-mpnet-base-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 384 tokens
• Number of Classes: 2 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
yes	'"Xi Jinping, China’s top leader, abandoned his “zero Covid” policy in early December. That policy had kept infections low but required costly precautions like mass testing — measures that exhausted the budgets of local governments."In a recent issue, The Economist magazine reported that China spent ~$250 billion on mass testing during a recent one-year period. The piece also indicated that an unnamed expert suggested that that number was likely to be much lower than the true amount. Even for China, this is a remarkable amount of resources devoted to that aspect of combating Covid. It's no wonder President Xi had to finally give up on zero Covid - in all its manifestations, China could no longer afford the strategy.\n' 'The huge excursions to and from China at the Dawn of 2020 for China's lunar year celebration, just after the Wuhan breakout in DEC 2019 and its aftermath of spreading Covid-19 as a wildfire across the globe has a lesson to compare the present situation.China's much advertised, the world's first stringent drive to eradicate Covid VIRUS by adopting "ZERO COVID " policy since 2019 was lifted on DEC,7,2022 after realizing its end point is a fiasco. The reporting 60k fatalities a week before the China's lunar year on 22,JAN,2023 is a caution to the international travelers. Any global viral spread in 2023 shan't become a justification for lifting Zero Covid policy and zero testing of the travelers- in and out by China.\n' 'Ace Not so black and white. China’s “No-COVID” policy during the early part of the pandemic, albeit draconian and heavy-handed, likely saved tens of thousands of lives. However, once vaccines became available, China should have 1) adopted Western mRNA vaccines which are more effective at preventing serious illness than the Chinese domestic versions. 2) Begin preparing for a gradual reopening by stockpiling antivirals to protect its most vulnerable citizens. By demonstrating the “superior” Chinese model with the prolonged strict no-COVID policy, President Xi was able to secure his unprecedented 3rd 5-year term.Liberals are against public health policies that are driven by political considerations rather than driven by science.\n'
no	'Teaching history is, by its very nature, a matter of prioritization and opinion. When it is a mandatory requirement for a high school diploma, the requirement to learn a specific version of history and regurgitate it becomes a form of indoctrination. DeSantis is an easy target for his opponents (I am one) for obvious reasons, but the challenge remains the same. What is the version of history that we want to teach our children? Should the history of black Americans be enhanced? What about Mexicans ( a largely overlooked group), women, Asians (nary a word about the Chinese Exclusion Act), religious subgroups - the early plight of Catholics, Jewish immigrants, Mormons, Muslims, and the emergence of a non-secular movement? How would we propose to teach about abortion rights? Is it the quiet revolution of the unborn or the destruction of rights previously available to women? The list goes on. I find articles like this with outrage dripping, reductive, and of little value. A challenge with public schools is that they are an arm of the government. So, it is hardly surprising that the CEO of the state/legislature would exert influence. A debate no history is highly valuable but America goes immediately to war with itself and no longer debates\n' "David Brook offers an interesting perspective on Biden and America's conduct in the world.Putin, Xi are all crazy people doing crazy things. In contrast, Biden is a steady hand guiding the American ship of the international rule based order.I suppose if I lived in the Washington bubble, I might have a similar view. But I come from a world of anti-imperialist struggle, and my world looks very different.I see the US undermining struggling nations all over the world, most recently in Africa. The ugly American fingerprints are also all over the coups in Honduras, Venezuela, Bolivia and Peru.Cuba is now in its sixtieth year of a crushing US blockade. US military bases now nun from Niger in West Africa, across the continent to Kenya.Active military operations are going on in Somalis, Syria and of course Ukraine.There's no difference between the referendums for autonomy held in Kosovo and the Donbas and Crimea, except that one was sponsored by the US and the other by Russia.According to the UN, world famine this year can be averted for 1.7 billion dollars. In contrast, our military funding for Ukraine is now at 122 billion.Under American leadership, corporations paid out $257bn to wealthy shareholders, while over 800 million people went to bed hungry.So, forgive me if I see Biden's ''steady hand” differently than the NYTimes crowd does.Perspective is everything, and the world looks very different when you see it from the bottom up.\n" 'LB and what would we do for our neighbors? What did we do when children were separated from their parents at the border under Trump? Most of us did nothing.\n'

Evaluation

Metrics

Label	Accuracy
all	1.0

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("davidadamczyk/setfit-model-8")
# Run inference
preds = model("China knows everything about its citizens, monitors every details in their lives but somehow can't say how many people exactly died from Covid19 since it ended its zero covid policy.Why should we believe these numbers instead of last week numbers?
")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	13	141.375	287

Label	Training Sample Count
no	18
yes	22

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 120
• 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
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0017	1	0.3089	-
0.0833	50	0.1005	-
0.1667	100	0.0014	-
0.25	150	0.0004	-
0.3333	200	0.0002	-
0.4167	250	0.0002	-
0.5	300	0.0002	-
0.5833	350	0.0001	-
0.6667	400	0.0001	-
0.75	450	0.0001	-
0.8333	500	0.0001	-
0.9167	550	0.0001	-
1.0	600	0.0001	-

Framework Versions

• Python: 3.10.13
• SetFit: 1.1.0
• Sentence Transformers: 3.0.1
• Transformers: 4.45.2
• PyTorch: 2.4.0+cu124
• Datasets: 2.21.0
• Tokenizers: 0.20.0

Citation

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}
}