setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 model-index endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: State and federal lawmakers are promising to improve conditions for hundreds
    of foreign fishermen working in Hawaii's commercial fleet, and at least one company
    has already stopped buying fish from the boats following an Associated Press investigation
    that found the men have been confined to vessels for years without basic labor
    protections.Whole Foods halted buying seafood caught by foreign crew until it's
    clear the men are treated fairly. On Sunday, the Hawaii Seafood Council said that
    starting Oct. 1, the Honolulu Fish Auction will sell fish only from boats that
    have adopted a new, standardized contract aimed at assuring no forced labor exists
    on board.The AP report found commercial fishing boats in Honolulu were crewed
    by men from impoverished Southeast Asia and Pacific Island nations who catch prized
    swordfish, ahi tuna and other seafood sold at markets and upscale restaurants
    across the country. A legal loophole allows them to work on the American-owned,
    American-flagged boats without visas as long as they don't set foot on shore.
    The system is facilitated by the U.S. Coast Guard and Customs and Border Protection.
    While many men appreciate the jobs, which pay better than they could get back
    home, the report revealed instances of human trafficking, tuberculosis and food
    shortages. It also found some fishermen being forced to defecate
- text:  Trinidad and Tobago is a destination, transit, and possible source country
    for adults and children subjected to sex trafficking and forced labor Women and
    girls from the Dominican Republic, Guyana, Venezuela, and Colombia are subjected
    to sex trafficking in brothels and clubs, with young women from Venezuela especially
    vulnerable. Economic migrants from the Caribbean region, especially Guyana, and
    from Asia are vulnerable to forced labor Victims have been subjected to forced
    labor in domestic service and the retail sector Immigration officials note an
    increase in international criminal organizations' involvement in trafficking,
    and NGOs report young boys are coerced to sell drugs and guns. In a break with
    common practice, some traffickers have recently allowed victims to keep their
    passports, removing a common indicator of human trafficking in an attempt to avoid
    detection. Many other traffickers continue to confiscate victims' passports and
    travel documents. Economic migrants who lack legal status may be exposed to various
    forms of exploitation and abuse indicative of trafficking. Trinidad and Tobago
    experiences a steady flow of vessels transiting its territorial waters, some of
    which may be engaged in illicit and illegal activities, including forced labor
    in the fishing industry. Complicity by police and immigration officials in trafficking
    crimes impeded anti-trafficking efforts. Law enforcement and civil society reported
- text: icked onto fishing boats. In early 2013, an organization that assists victims
    in Cambodia assessed this form of trafficking was rising. Cambodian and Burmese
    workers are increasingly unwilling to work in the Thai fishing industry due to
    dangerous work conditions and isolation, which makes them more vulnerable to exploitation;
    the Government of Thailand announced plans during the year to import Bangladeshi
    workers to ill the labor shortage this has caused. During the year, therewere
    reports that some Rohingya asylum seekers from Burma were smuggled into Thailand
    en route to Malaysia and ultimately sold into forced labor, allegedly with the
    assistance of Thai civilian and military officials.Observers noted that traffickers
    (including labor brokers) who bring foreign victims into Thailand generally work
    as individuals or in unorganized groups, while those who exploit Thai victims
    abroad tend to be more organized. Labor brokers, largely unregulated, serve as
    intermediaries between jobseekers and employers; some facilitate or engage in
    human trafficking. Brokers are reportedly of both Thai and foreign origin and
    work in networks, collaborating with employers and attimes with corrupt law enforcement
    officials. Foreign migrants, members of ethnic minorities, and stateless persons
    in Thailand are at the greatest risk of being trafficked, and they experience
    the withholding of travel documents
- text: ' in their own villages by debt bondage or born ito slavery, work in construction,
    textiles, brick-making, mines, fish and prawn processing and hospitality.Russia490,000
    - 540,000Migrant workers endure extortion and physical abuse; anecdotal evidence
    suggests that forced labour camps still operate in Siberia.China2,800,000 - 3,100,000Severe
    forced labour in brick kilns in the north; forced labour in modern industries
    including fashion and computer supply chains.Myanmar360,000 - 400,000Slavery includes
    reports of deceptive recruitment of women for sale as brides in China, forced
    labour of adults on plantations and in industry and forced labour of children
    in tea shops, home industries and as beggars.Thailand450,000 - 500,000An explosion
    in global demand for seafood has led to an increased need for cheap migrant labour,
    including on fishing boats. High numbers of children are exploited, particulary
    those from ethnic minorities and hill tribes.SOURCE: THE GLOBAL SLAVERY INDEX
    2013'
- text: The number of Cambodians recently found in Indonesia after being trafficked
    onto Thai fishing vessels has risen to 230, the Ministry of Foreign Affairs said
    in a press statement released yesterday. The ministry confirmed that, following
    an investigation by Indonesian authorities along with Cambodian Embassy personnel,
    an additional 31 fishermen were rescued from Ambon Island over the last week,
    adding to the 199 discovered last Friday. The men were reportedly trafficked to
    work on the Thai vessels for years before Indonesian authorities managed to rescue
    them.Ministry spokesman Koy Kuong said Cambodian officials visited the island
    from May 30 to June 3 to check on the men's conditions, adding that the owner
    of the Thai fishing boats have paid the workers their salary and have agreed to
    pay for a charter flight from Ambon to Phnom Penh.They have agreed in principle,
    and now they are processing the procedure to ensure that these people to return
    sometime this month, he said.International Organisation for Migration project
    manager Paul Dillon said IOM staff had joined a small mission from the Ministry
    of Fisheries and Oceans [Thursday] at the Indonesian government's request on a
    fact-finding mission .â(EURO)°.â(EURO)°. to identify possible
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model 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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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 [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **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)

### Model Labels
| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>' due diligence and weak monitoring control, surveillance and enforcement systems by coastal, flag, and port states," it noted.Human rights abuses are also driven by an array of other factors, it added. The Thai fishing industry is structurally dependent on unskilled workers, a result of a failure to invest in technology to increase labour productivity as well as an abundance of cheap migrant workers from the less-developed neighbours.At the same time, vessel operators face a chronic shortage of workers -- a deficit estimated by the National Fishing Association of Thailand (NFAT) to be as high as 50,000."Combined with economic pressure arising from the degradation of marine resources in the Thai exclusive economic zone (EEZ), these factors shape the prevalence of labour abuses and the use of trafficking, forced and bonded labour in the Thai fishing industry."As migrant workers from Myanmar, Cambodia, Laos and undeveloped rural regions of Thailand, particularly the Northeast, are trafficked through Indonesia, both countries must collaborate further to address abuses, said Mark Dia, Greenpeace\'s Regional Oceans Campaign coordinator for Southeast Asia.Greenpeace has worked with the Indonesian government on the matter while the Thai military government has already taken action to address chronic problems facing the fishing industry for many years'</li><li>' for all forms of trafficking, including forced and bonded labour, respecting due process.Forced labour constitutes India\'s largest trafficking problem; men, women, and children in debt bondage- sometimes inherited from previous generations- are forced to work in brick kilns, rice mills, agriculture, and embroidery units, it said.The majority of India\'s trafficking problem is internal, and those from the most disadvantaged social strata- Dalits, members of tribal communities, religious minorities, and women and girls from excluded groups- are most vulnerable, it added."Within India, some are subjected to forced labour in sectors such as construction, steel, and textile industries; wire manufacturing for underground cables; biscuit factories; pickling; floriculture; fish farms; and ship breaking," said the State Department.Thousands of unregulated work placement agencies reportedly lure adults and children under false promises of employment for sex trafficking or forced labour, including domestic servitude.In addition to bonded labour, some children are subjected to forced labour as factory and agricultural workers, domestic servants, and beggars. Begging ringleaders sometimes maim children to earn more money."Some NGOs and media report girls are sold and forced to conceive and deliver babies for sale. Conditions amounting to'</li><li>" supplies.\xa0Americans buying Hawaiian seafood are almost certainly eating fish caught by one of these workers.'We want the same standards as the other workers in America, but we are just small people working there,' said fisherman Syamsul Maarif, who didn't get paid for four months.\xa0He was sent back to his Indonesian village after nearly dying at sea when his Hawaiian boat sank earlier this year.Because they have no visas, the men can't fly into Hawaii, so they're brought by boat.\xa0And since they are not technically in the country, they're at the mercy of their American captains on American-flagged, American-owned vessels, catching choice swordfish and ahi tuna that can fetch more than $1,000 apiece.\xa0The entire system contradicts other state and federal laws, yet operates with the blessing of U.S. officials and law enforcement.'People say these fishermen can't leave their boats, they're like captives,' said U.S. Attorney Florence Nakakuni in Hawaii.\xa0'But they don't have visas, so they can't leave their boat, really.'Each of the roughly 140 boats in the fleet docks about once every three weeks, occasionally at ports"</li></ul> |
| 0     | <ul><li>' forced marriage and other such exploitation. In a DW interview, Fiona David, executive director of global research at the Walk Free Foundation, calls on the countries in the region to step up their efforts to combat the problem and put in place the mechanisms that would require businesses to focus on the issues of slavery and forced labor throughout their supply chains. DW: According to the 2016 Global Slavery Index, nearly two-thirds of modern slaves are living in Asian countries. What are the reasons behind the high prevalence of this phenomenon in the region? Fiona David: The Asia-Pacific is the most populous region in the world, and it is also well integrated into the global supply chains. We do estimate that about two-thirds of the nearly 46 million people trapped in slavery are in Asia. And we see all forms of modern slavery in the region, such as forced labor in brick kilns, child beggars in Afghanistan and India, bonded labor in the agricultural as well as garment sectors. Given its population size and integration into global value chains, the Asia-Pacific is a region where a lot of low cost labor is made available to produce the goods and services that we all consume. What kind of living and working conditions do these people find themselves in? They experience miserable'</li><li>" going to cook those up for\xa0\xa0\xa0\xa0\xa0 dinner?' I said, no, that's bait for fishing'. He thought we were\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0 going to cook and eat those frozen prawns!\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Signs to look for when fish are biting, as well as learning about how to catch and name fish, are among other activities, as is counting and comparing the yield, talking about how many fish should be cooked on the spot, or taken home to family members. In addition, there is the preparation and cooking of the fish once caught: gutting and singeing it when required, making a coal fire, building a bush oven, timing the bake, turning the fish, and finding leaves and bark from local trees to plate cooked fish (Toussaint 2010; Toussaint et al. 2005; Yu 2006). The eating and sharing of fish, enjoying its tastiness, or comparing the quality of one fish to another, marks the culmination of a good family day, a point I develop below. All of these activities, whether seen on their own as individual parts, or brought together as a whole, reveal their experiential importance to those families who are directly involved, as well"</li><li>"8 million in modern slaveryPakistan2,000,000 - 2,200,000Bonded labour affects men, women and children largely from rural areas who travel to cities to find work, and has been reported in many industries, primarily brick kilns, but also in agriculture, fisheries and mining.Ethiopia620,000 - 680,000Domestic workers travelling under illegal private employment agencies are particularly vulnerable as are girls who can be subjected to child marriage.Nigeria670,000 - 740,000An estimated 15.88% of the estimated total 29.6 million people in modern slavery are in Sub-Saharan Africa.Bangladesh330,000 - 360,000Large numbers of women and girls are reportedly trafficked to India and Pakistan annually and children, including boys, are exploited and trafficked for sex and labour.Democratic Republic of Congo440,000 - 490,000One of the world's poorest countries, despite a wealth of resources; 90% of men working in mines in eastern DRC are trapped by debt bondage.India13,300,000 - 14,700,000Men, women and children, many enslaved"</li></ul>                                                                                                                                                                                                                                                                                                                                                                 |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("JoshELambert/forced-labor")
# Run inference
preds = model(" in their own villages by debt bondage or born ito slavery, work in construction, textiles, brick-making, mines, fish and prawn processing and hospitality.Russia490,000 - 540,000Migrant workers endure extortion and physical abuse; anecdotal evidence suggests that forced labour camps still operate in Siberia.China2,800,000 - 3,100,000Severe forced labour in brick kilns in the north; forced labour in modern industries including fashion and computer supply chains.Myanmar360,000 - 400,000Slavery includes reports of deceptive recruitment of women for sale as brides in China, forced labour of adults on plantations and in industry and forced labour of children in tea shops, home industries and as beggars.Thailand450,000 - 500,000An explosion in global demand for seafood has led to an increased need for cheap migrant labour, including on fishing boats. High numbers of children are exploited, particulary those from ethnic minorities and hill tribes.SOURCE: THE GLOBAL SLAVERY INDEX 2013")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median   | Max |
|:-------------|:----|:---------|:----|
| Word count   | 50  | 189.8442 | 221 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 8                     |
| 1     | 69                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0033 | 1    | 0.1808        | -               |
| 0.1629 | 50   | 0.1363        | -               |
| 0.3257 | 100  | 0.0103        | -               |
| 0.4886 | 150  | 0.0019        | -               |
| 0.6515 | 200  | 0.0005        | -               |
| 0.8143 | 250  | 0.0001        | -               |
| 0.9772 | 300  | 0.0           | -               |
| 1.0    | 307  | -             | 0.0407          |
| 1.1401 | 350  | 0.0001        | -               |
| 1.3029 | 400  | 0.0           | -               |
| 1.4658 | 450  | 0.0           | -               |
| 1.6287 | 500  | 0.0           | -               |
| 1.7915 | 550  | 0.0           | -               |
| 1.9544 | 600  | 0.0           | -               |
| 2.0    | 614  | -             | 0.0272          |
| 2.1173 | 650  | 0.0           | -               |
| 2.2801 | 700  | 0.0           | -               |
| 2.4430 | 750  | 0.0           | -               |
| 2.6059 | 800  | 0.0           | -               |
| 2.7687 | 850  | 0.0           | -               |
| 2.9316 | 900  | 0.0           | -               |
| 3.0    | 921  | -             | 0.0238          |
| 3.0945 | 950  | 0.0           | -               |
| 3.2573 | 1000 | 0.0           | -               |
| 3.4202 | 1050 | 0.0           | -               |
| 3.5831 | 1100 | 0.0           | -               |
| 3.7459 | 1150 | 0.0           | -               |
| 3.9088 | 1200 | 0.0           | -               |
| 4.0    | 1228 | -             | 0.0227          |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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