---
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: Constitutional changes prohibit selling agricultural land to foreigners.
- text: Administrative procedures for outward direct investments were simplified.
Direct investment projects no longer have to be referred to the CBC for verification
of genuineness prior to the transfer of funds through authorized dealers, unless
the required amount exceeds £C 5 million a year.
- text: In addition, nonbank corporation which borrow abroad must fulfill certain
credit rating criteria.
- text: The threshold above which there are no controls on direct investments by companies
not listed publicly was reduced to 50% of capital from two-thirds. In the case
of companies whose shares are listed on the stock exchange, the threshold was
increased to 50% of capital from 20%.
- text: The limits on purchases of foreign securities by insurance companies were
increased to 30% from 25% of technical provisions and risk capital reserves.
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: 0.828125
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:** 3 classes
### 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 |
|:--------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neutral | - 'Administrative Resolution APS/DJ/DI/No. 221/2021 establishes that for fiscal year 2021 the maximum limit for investments by insurance companies set by the Central Bank of Bolivia includes investments in debt securities issued abroad by the National General Treasury (Bolivian Sovereign Bonds).'
- 'Decree No. 04 of the head of the Insurance State Supervision Service replaced Decree No. 51/01 of the president of the National Bank of Georgia as the regulation governing insurance companies and pension funds.'
- 'The Law on the Securities Market was promulgated, setting out the legal framework governing capital market operations.'
|
| loosen | - 'Limits on transfers abroad were increased. Depositors may transfer within their normal business activity and on presentation of supporting documents up to €20,000 a day an account without approval and €20,001 to €300,000 a transaction with approval by the committee established by the MOF and the Central Bank of Cyprus based on the liquidity buffer situation of the bank. Amounts exceeding €300,000 a transaction require individual approval of the committee.'
- 'According to the Consolidated Law of Finance (Legislative Decree No. 58 of February 24, 1998) as amended by Legislative Decree No. 44/2014, if mutual funds are covered under EU directives (UCITS and European Directive on Alternative Investment Fund Managers), the CONSOB must be notified before the offering. Under the same amendment, offering securities issued by mutual funds that are not covered under EU directives is not allowed. Previously, offerings of securities issued by mutual funds not covered under EU directives were subject to authorization.'
- 'The capital outflow tax was reduced to 4.25% from 4.50%.'
|
| tighten | - 'Bank Indonesia Regulation No. 14/25/PBI/2012 of December 27, 2012, concerning Receipt of Export Proceeds and Withdrawal of Foreign Currency on External Debt refines Bank Indonesia Regulations Nos. 13/20/PBI/2011 and 14/11/PBI/2012. The new regulation aims to ensure that receipt of foreign exchange proceeds from debt issuance abroad takes place through a domestic foreign exchange bank in the Indonesian banking system. These funds do not have to be kept in a domestic bank and they may be freely transferred abroad. The foreign exchange does not have to be converted to domestic currency. This requirement did not apply to agreements signed before January 2, 2012, during a transition period that ended December 31, 2012.'
- 'Investment Proclamation No. 1180/2020 introduced a new framework to register and administer foreign direct investment. It included provisions for a minimum capital amount per project a foreign investor must invest ranging from US$50,000 to US$200,000 depending on the type of investment being undertaken.'
- 'External debt of publicly owned enterprises must be approved by the Superior Strategic Council of Publicly Owned Enterprises (Consejo Superior Estratégico de la Empresa Pública—COSEEP) pursuant to Law No. 466 of December 27, 2013.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8281 |
## 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("kteoh37/setfit_sectionxi_changes_100_examples")
# Run inference
preds = model("Constitutional changes prohibit selling agricultural land to foreigners.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 6 | 38.4936 | 225 |
| Label | Training Sample Count |
|:--------|:----------------------|
| loosen | 100 |
| tighten | 100 |
| neutral | 35 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (10, 10)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0005 | 1 | 0.2629 | - |
| 0.0235 | 50 | 0.2148 | - |
| 0.0471 | 100 | 0.2342 | - |
| 0.0706 | 150 | 0.2326 | - |
| 0.0941 | 200 | 0.2391 | - |
| 0.1176 | 250 | 0.2351 | - |
| 0.1412 | 300 | 0.1484 | - |
| 0.1647 | 350 | 0.1034 | - |
| 0.1882 | 400 | 0.123 | - |
| 0.2118 | 450 | 0.064 | - |
| 0.2353 | 500 | 0.0697 | - |
| 0.2588 | 550 | 0.0367 | - |
| 0.2824 | 600 | 0.0695 | - |
| 0.3059 | 650 | 0.0044 | - |
| 0.3294 | 700 | 0.009 | - |
| 0.3529 | 750 | 0.0027 | - |
| 0.3765 | 800 | 0.0012 | - |
| 0.4 | 850 | 0.0609 | - |
| 0.4235 | 900 | 0.0019 | - |
| 0.4471 | 950 | 0.0013 | - |
| 0.4706 | 1000 | 0.0031 | - |
| 0.4941 | 1050 | 0.0004 | - |
| 0.5176 | 1100 | 0.0029 | - |
| 0.5412 | 1150 | 0.001 | - |
| 0.5647 | 1200 | 0.0018 | - |
| 0.5882 | 1250 | 0.0023 | - |
| 0.6118 | 1300 | 0.0003 | - |
| 0.6353 | 1350 | 0.0007 | - |
| 0.6588 | 1400 | 0.0204 | - |
| 0.6824 | 1450 | 0.0486 | - |
| 0.7059 | 1500 | 0.0056 | - |
| 0.7294 | 1550 | 0.0003 | - |
| 0.7529 | 1600 | 0.0052 | - |
| 0.7765 | 1650 | 0.0033 | - |
| 0.8 | 1700 | 0.0021 | - |
| 0.8235 | 1750 | 0.0003 | - |
| 0.8471 | 1800 | 0.0111 | - |
| 0.8706 | 1850 | 0.0003 | - |
| 0.8941 | 1900 | 0.0531 | - |
| 0.9176 | 1950 | 0.0075 | - |
| 0.9412 | 2000 | 0.0003 | - |
| 0.9647 | 2050 | 0.0022 | - |
| 0.9882 | 2100 | 0.0006 | - |
| **1.0** | **2125** | **-** | **0.1384** |
| 1.0118 | 2150 | 0.0001 | - |
| 1.0353 | 2200 | 0.0001 | - |
| 1.0588 | 2250 | 0.0011 | - |
| 1.0824 | 2300 | 0.0001 | - |
| 1.1059 | 2350 | 0.0002 | - |
| 1.1294 | 2400 | 0.0 | - |
| 1.1529 | 2450 | 0.0002 | - |
| 1.1765 | 2500 | 0.0003 | - |
| 1.2 | 2550 | 0.0116 | - |
| 1.2235 | 2600 | 0.0002 | - |
| 1.2471 | 2650 | 0.0002 | - |
| 1.2706 | 2700 | 0.0001 | - |
| 1.2941 | 2750 | 0.0002 | - |
| 1.3176 | 2800 | 0.0 | - |
| 1.3412 | 2850 | 0.0132 | - |
| 1.3647 | 2900 | 0.0265 | - |
| 1.3882 | 2950 | 0.0035 | - |
| 1.4118 | 3000 | 0.0003 | - |
| 1.4353 | 3050 | 0.0022 | - |
| 1.4588 | 3100 | 0.0013 | - |
| 1.4824 | 3150 | 0.0006 | - |
| 1.5059 | 3200 | 0.0003 | - |
| 1.5294 | 3250 | 0.0 | - |
| 1.5529 | 3300 | 0.0198 | - |
| 1.5765 | 3350 | 0.0001 | - |
| 1.6 | 3400 | 0.0 | - |
| 1.6235 | 3450 | 0.0001 | - |
| 1.6471 | 3500 | 0.0 | - |
| 1.6706 | 3550 | 0.0 | - |
| 1.6941 | 3600 | 0.0002 | - |
| 1.7176 | 3650 | 0.0 | - |
| 1.7412 | 3700 | 0.0 | - |
| 1.7647 | 3750 | 0.0023 | - |
| 1.7882 | 3800 | 0.0 | - |
| 1.8118 | 3850 | 0.0074 | - |
| 1.8353 | 3900 | 0.0004 | - |
| 1.8588 | 3950 | 0.0001 | - |
| 1.8824 | 4000 | 0.0228 | - |
| 1.9059 | 4050 | 0.0256 | - |
| 1.9294 | 4100 | 0.0316 | - |
| 1.9529 | 4150 | 0.0001 | - |
| 1.9765 | 4200 | 0.0 | - |
| 2.0 | 4250 | 0.0 | 0.1386 |
| 2.0235 | 4300 | 0.0006 | - |
| 2.0471 | 4350 | 0.0001 | - |
| 2.0706 | 4400 | 0.0072 | - |
| 2.0941 | 4450 | 0.0433 | - |
| 2.1176 | 4500 | 0.0001 | - |
| 2.1412 | 4550 | 0.0004 | - |
| 2.1647 | 4600 | 0.0 | - |
| 2.1882 | 4650 | 0.0024 | - |
| 2.2118 | 4700 | 0.0 | - |
| 2.2353 | 4750 | 0.0001 | - |
| 2.2588 | 4800 | 0.0 | - |
| 2.2824 | 4850 | 0.0002 | - |
| 2.3059 | 4900 | 0.0001 | - |
| 2.3294 | 4950 | 0.0 | - |
| 2.3529 | 5000 | 0.002 | - |
| 2.3765 | 5050 | 0.0303 | - |
| 2.4 | 5100 | 0.0799 | - |
| 2.4235 | 5150 | 0.0001 | - |
| 2.4471 | 5200 | 0.0 | - |
| 2.4706 | 5250 | 0.0024 | - |
| 2.4941 | 5300 | 0.0001 | - |
| 2.5176 | 5350 | 0.0138 | - |
| 2.5412 | 5400 | 0.0 | - |
| 2.5647 | 5450 | 0.0001 | - |
| 2.5882 | 5500 | 0.0 | - |
| 2.6118 | 5550 | 0.0 | - |
| 2.6353 | 5600 | 0.0 | - |
| 2.6588 | 5650 | 0.0 | - |
| 2.6824 | 5700 | 0.0396 | - |
| 2.7059 | 5750 | 0.0001 | - |
| 2.7294 | 5800 | 0.0 | - |
| 2.7529 | 5850 | 0.0002 | - |
| 2.7765 | 5900 | 0.0001 | - |
| 2.8 | 5950 | 0.0037 | - |
| 2.8235 | 6000 | 0.0 | - |
| 2.8471 | 6050 | 0.0186 | - |
| 2.8706 | 6100 | 0.0043 | - |
| 2.8941 | 6150 | 0.0315 | - |
| 2.9176 | 6200 | 0.0144 | - |
| 2.9412 | 6250 | 0.0 | - |
| 2.9647 | 6300 | 0.0052 | - |
| 2.9882 | 6350 | 0.0003 | - |
| 3.0 | 6375 | - | 0.1526 |
| 3.0118 | 6400 | 0.0 | - |
| 3.0353 | 6450 | 0.0002 | - |
| 3.0588 | 6500 | 0.0011 | - |
| 3.0824 | 6550 | 0.0 | - |
| 3.1059 | 6600 | 0.0 | - |
| 3.1294 | 6650 | 0.0002 | - |
| 3.1529 | 6700 | 0.0001 | - |
| 3.1765 | 6750 | 0.0002 | - |
| 3.2 | 6800 | 0.0191 | - |
| 3.2235 | 6850 | 0.0001 | - |
| 3.2471 | 6900 | 0.0 | - |
| 3.2706 | 6950 | 0.0036 | - |
| 3.2941 | 7000 | 0.0001 | - |
| 3.3176 | 7050 | 0.0197 | - |
| 3.3412 | 7100 | 0.0101 | - |
| 3.3647 | 7150 | 0.0 | - |
| 3.3882 | 7200 | 0.0 | - |
| 3.4118 | 7250 | 0.0003 | - |
| 3.4353 | 7300 | 0.0001 | - |
| 3.4588 | 7350 | 0.0 | - |
| 3.4824 | 7400 | 0.0001 | - |
| 3.5059 | 7450 | 0.0174 | - |
| 3.5294 | 7500 | 0.0 | - |
| 3.5529 | 7550 | 0.0 | - |
| 3.5765 | 7600 | 0.0 | - |
| 3.6 | 7650 | 0.0 | - |
| 3.6235 | 7700 | 0.0012 | - |
| 3.6471 | 7750 | 0.0 | - |
| 3.6706 | 7800 | 0.0 | - |
| 3.6941 | 7850 | 0.0 | - |
| 3.7176 | 7900 | 0.0 | - |
| 3.7412 | 7950 | 0.0 | - |
| 3.7647 | 8000 | 0.0 | - |
| 3.7882 | 8050 | 0.0 | - |
| 3.8118 | 8100 | 0.0 | - |
| 3.8353 | 8150 | 0.0004 | - |
| 3.8588 | 8200 | 0.0 | - |
| 3.8824 | 8250 | 0.0154 | - |
| 3.9059 | 8300 | 0.0201 | - |
| 3.9294 | 8350 | 0.0332 | - |
| 3.9529 | 8400 | 0.0 | - |
| 3.9765 | 8450 | 0.0002 | - |
| 4.0 | 8500 | 0.0028 | 0.1434 |
| 4.0235 | 8550 | 0.0001 | - |
| 4.0471 | 8600 | 0.0 | - |
| 4.0706 | 8650 | 0.0077 | - |
| 4.0941 | 8700 | 0.0435 | - |
| 4.1176 | 8750 | 0.0 | - |
| 4.1412 | 8800 | 0.0001 | - |
| 4.1647 | 8850 | 0.0 | - |
| 4.1882 | 8900 | 0.0024 | - |
| 4.2118 | 8950 | 0.0 | - |
| 4.2353 | 9000 | 0.0 | - |
| 4.2588 | 9050 | 0.0 | - |
| 4.2824 | 9100 | 0.0002 | - |
| 4.3059 | 9150 | 0.0 | - |
| 4.3294 | 9200 | 0.0263 | - |
| 4.3529 | 9250 | 0.0 | - |
| 4.3765 | 9300 | 0.0 | - |
| 4.4 | 9350 | 0.0416 | - |
| 4.4235 | 9400 | 0.0 | - |
| 4.4471 | 9450 | 0.0061 | - |
| 4.4706 | 9500 | 0.0121 | - |
| 4.4941 | 9550 | 0.0001 | - |
| 4.5176 | 9600 | 0.0187 | - |
| 4.5412 | 9650 | 0.0 | - |
| 4.5647 | 9700 | 0.0 | - |
| 4.5882 | 9750 | 0.0 | - |
| 4.6118 | 9800 | 0.0 | - |
| 4.6353 | 9850 | 0.0 | - |
| 4.6588 | 9900 | 0.0117 | - |
| 4.6824 | 9950 | 0.0367 | - |
| 4.7059 | 10000 | 0.006 | - |
| 4.7294 | 10050 | 0.0 | - |
| 4.7529 | 10100 | 0.0002 | - |
| 4.7765 | 10150 | 0.0003 | - |
| 4.8 | 10200 | 0.0 | - |
| 4.8235 | 10250 | 0.0 | - |
| 4.8471 | 10300 | 0.0085 | - |
| 4.8706 | 10350 | 0.0 | - |
| 4.8941 | 10400 | 0.0369 | - |
| 4.9176 | 10450 | 0.0091 | - |
| 4.9412 | 10500 | 0.0 | - |
| 4.9647 | 10550 | 0.0 | - |
| 4.9882 | 10600 | 0.0 | - |
| 5.0 | 10625 | - | 0.1711 |
| 5.0118 | 10650 | 0.0 | - |
| 5.0353 | 10700 | 0.0 | - |
| 5.0588 | 10750 | 0.0 | - |
| 5.0824 | 10800 | 0.0238 | - |
| 5.1059 | 10850 | 0.0 | - |
| 5.1294 | 10900 | 0.0075 | - |
| 5.1529 | 10950 | 0.0 | - |
| 5.1765 | 11000 | 0.0 | - |
| 5.2 | 11050 | 0.0179 | - |
| 5.2235 | 11100 | 0.0 | - |
| 5.2471 | 11150 | 0.0 | - |
| 5.2706 | 11200 | 0.0171 | - |
| 5.2941 | 11250 | 0.0002 | - |
| 5.3176 | 11300 | 0.0 | - |
| 5.3412 | 11350 | 0.0128 | - |
| 5.3647 | 11400 | 0.0 | - |
| 5.3882 | 11450 | 0.0029 | - |
| 5.4118 | 11500 | 0.0 | - |
| 5.4353 | 11550 | 0.0 | - |
| 5.4588 | 11600 | 0.0 | - |
| 5.4824 | 11650 | 0.0 | - |
| 5.5059 | 11700 | 0.0 | - |
| 5.5294 | 11750 | 0.0 | - |
| 5.5529 | 11800 | 0.0 | - |
| 5.5765 | 11850 | 0.0001 | - |
| 5.6 | 11900 | 0.0001 | - |
| 5.6235 | 11950 | 0.0001 | - |
| 5.6471 | 12000 | 0.0 | - |
| 5.6706 | 12050 | 0.0001 | - |
| 5.6941 | 12100 | 0.0 | - |
| 5.7176 | 12150 | 0.0 | - |
| 5.7412 | 12200 | 0.0 | - |
| 5.7647 | 12250 | 0.0 | - |
| 5.7882 | 12300 | 0.006 | - |
| 5.8118 | 12350 | 0.0001 | - |
| 5.8353 | 12400 | 0.0042 | - |
| 5.8588 | 12450 | 0.0001 | - |
| 5.8824 | 12500 | 0.0 | - |
| 5.9059 | 12550 | 0.017 | - |
| 5.9294 | 12600 | 0.0282 | - |
| 5.9529 | 12650 | 0.0 | - |
| 5.9765 | 12700 | 0.0046 | - |
| 6.0 | 12750 | 0.0 | 0.1451 |
| 6.0235 | 12800 | 0.0001 | - |
| 6.0471 | 12850 | 0.0002 | - |
| 6.0706 | 12900 | 0.0625 | - |
| 6.0941 | 12950 | 0.0633 | - |
| 6.1176 | 13000 | 0.0598 | - |
| 6.1412 | 13050 | 0.0001 | - |
| 6.1647 | 13100 | 0.0012 | - |
| 6.1882 | 13150 | 0.0004 | - |
| 6.2118 | 13200 | 0.0 | - |
| 6.2353 | 13250 | 0.0002 | - |
| 6.2588 | 13300 | 0.0 | - |
| 6.2824 | 13350 | 0.0608 | - |
| 6.3059 | 13400 | 0.0006 | - |
| 6.3294 | 13450 | 0.0 | - |
| 6.3529 | 13500 | 0.0587 | - |
| 6.3765 | 13550 | 0.0003 | - |
| 6.4 | 13600 | 0.0429 | - |
| 6.4235 | 13650 | 0.0 | - |
| 6.4471 | 13700 | 0.0 | - |
| 6.4706 | 13750 | 0.0001 | - |
| 6.4941 | 13800 | 0.0 | - |
| 6.5176 | 13850 | 0.0135 | - |
| 6.5412 | 13900 | 0.019 | - |
| 6.5647 | 13950 | 0.0028 | - |
| 6.5882 | 14000 | 0.0 | - |
| 6.6118 | 14050 | 0.0 | - |
| 6.6353 | 14100 | 0.0169 | - |
| 6.6588 | 14150 | 0.0167 | - |
| 6.6824 | 14200 | 0.0375 | - |
| 6.7059 | 14250 | 0.0 | - |
| 6.7294 | 14300 | 0.0044 | - |
| 6.7529 | 14350 | 0.0 | - |
| 6.7765 | 14400 | 0.0 | - |
| 6.8 | 14450 | 0.0025 | - |
| 6.8235 | 14500 | 0.0033 | - |
| 6.8471 | 14550 | 0.0145 | - |
| 6.8706 | 14600 | 0.0 | - |
| 6.8941 | 14650 | 0.0346 | - |
| 6.9176 | 14700 | 0.0117 | - |
| 6.9412 | 14750 | 0.0001 | - |
| 6.9647 | 14800 | 0.0 | - |
| 6.9882 | 14850 | 0.0 | - |
| 7.0 | 14875 | - | 0.1828 |
| 7.0118 | 14900 | 0.0 | - |
| 7.0353 | 14950 | 0.0 | - |
| 7.0588 | 15000 | 0.0031 | - |
| 7.0824 | 15050 | 0.0001 | - |
| 7.1059 | 15100 | 0.0055 | - |
| 7.1294 | 15150 | 0.0208 | - |
| 7.1529 | 15200 | 0.0 | - |
| 7.1765 | 15250 | 0.0 | - |
| 7.2 | 15300 | 0.0134 | - |
| 7.2235 | 15350 | 0.0 | - |
| 7.2471 | 15400 | 0.0222 | - |
| 7.2706 | 15450 | 0.0 | - |
| 7.2941 | 15500 | 0.0001 | - |
| 7.3176 | 15550 | 0.0 | - |
| 7.3412 | 15600 | 0.0111 | - |
| 7.3647 | 15650 | 0.0 | - |
| 7.3882 | 15700 | 0.0025 | - |
| 7.4118 | 15750 | 0.0 | - |
| 7.4353 | 15800 | 0.0 | - |
| 7.4588 | 15850 | 0.0 | - |
| 7.4824 | 15900 | 0.0 | - |
| 7.5059 | 15950 | 0.0 | - |
| 7.5294 | 16000 | 0.0 | - |
| 7.5529 | 16050 | 0.0 | - |
| 7.5765 | 16100 | 0.0 | - |
| 7.6 | 16150 | 0.0 | - |
| 7.6235 | 16200 | 0.0001 | - |
| 7.6471 | 16250 | 0.0203 | - |
| 7.6706 | 16300 | 0.0 | - |
| 7.6941 | 16350 | 0.0 | - |
| 7.7176 | 16400 | 0.0 | - |
| 7.7412 | 16450 | 0.0186 | - |
| 7.7647 | 16500 | 0.0 | - |
| 7.7882 | 16550 | 0.0 | - |
| 7.8118 | 16600 | 0.0049 | - |
| 7.8353 | 16650 | 0.0 | - |
| 7.8588 | 16700 | 0.0044 | - |
| 7.8824 | 16750 | 0.0266 | - |
| 7.9059 | 16800 | 0.015 | - |
| 7.9294 | 16850 | 0.0331 | - |
| 7.9529 | 16900 | 0.0 | - |
| 7.9765 | 16950 | 0.0 | - |
| 8.0 | 17000 | 0.0 | 0.1778 |
| 8.0235 | 17050 | 0.0 | - |
| 8.0471 | 17100 | 0.0 | - |
| 8.0706 | 17150 | 0.0082 | - |
| 8.0941 | 17200 | 0.0414 | - |
| 8.1176 | 17250 | 0.0 | - |
| 8.1412 | 17300 | 0.0 | - |
| 8.1647 | 17350 | 0.0025 | - |
| 8.1882 | 17400 | 0.0 | - |
| 8.2118 | 17450 | 0.0 | - |
| 8.2353 | 17500 | 0.0 | - |
| 8.2588 | 17550 | 0.0 | - |
| 8.2824 | 17600 | 0.0 | - |
| 8.3059 | 17650 | 0.0 | - |
| 8.3294 | 17700 | 0.0033 | - |
| 8.3529 | 17750 | 0.0 | - |
| 8.3765 | 17800 | 0.0033 | - |
| 8.4 | 17850 | 0.0371 | - |
| 8.4235 | 17900 | 0.0217 | - |
| 8.4471 | 17950 | 0.004 | - |
| 8.4706 | 18000 | 0.0 | - |
| 8.4941 | 18050 | 0.0 | - |
| 8.5176 | 18100 | 0.0179 | - |
| 8.5412 | 18150 | 0.0 | - |
| 8.5647 | 18200 | 0.0 | - |
| 8.5882 | 18250 | 0.0032 | - |
| 8.6118 | 18300 | 0.0 | - |
| 8.6353 | 18350 | 0.0026 | - |
| 8.6588 | 18400 | 0.0 | - |
| 8.6824 | 18450 | 0.0387 | - |
| 8.7059 | 18500 | 0.0 | - |
| 8.7294 | 18550 | 0.0 | - |
| 8.7529 | 18600 | 0.0204 | - |
| 8.7765 | 18650 | 0.0212 | - |
| 8.8 | 18700 | 0.0 | - |
| 8.8235 | 18750 | 0.0065 | - |
| 8.8471 | 18800 | 0.0114 | - |
| 8.8706 | 18850 | 0.0 | - |
| 8.8941 | 18900 | 0.0335 | - |
| 8.9176 | 18950 | 0.0147 | - |
| 8.9412 | 19000 | 0.0 | - |
| 8.9647 | 19050 | 0.0053 | - |
| 8.9882 | 19100 | 0.0 | - |
| 9.0 | 19125 | - | 0.1773 |
| 9.0118 | 19150 | 0.0181 | - |
| 9.0353 | 19200 | 0.0 | - |
| 9.0588 | 19250 | 0.0 | - |
| 9.0824 | 19300 | 0.0 | - |
| 9.1059 | 19350 | 0.0226 | - |
| 9.1294 | 19400 | 0.0 | - |
| 9.1529 | 19450 | 0.0 | - |
| 9.1765 | 19500 | 0.0 | - |
| 9.2 | 19550 | 0.013 | - |
| 9.2235 | 19600 | 0.0036 | - |
| 9.2471 | 19650 | 0.0 | - |
| 9.2706 | 19700 | 0.0 | - |
| 9.2941 | 19750 | 0.0 | - |
| 9.3176 | 19800 | 0.0 | - |
| 9.3412 | 19850 | 0.0537 | - |
| 9.3647 | 19900 | 0.0 | - |
| 9.3882 | 19950 | 0.0031 | - |
| 9.4118 | 20000 | 0.0 | - |
| 9.4353 | 20050 | 0.0 | - |
| 9.4588 | 20100 | 0.0 | - |
| 9.4824 | 20150 | 0.0 | - |
| 9.5059 | 20200 | 0.0 | - |
| 9.5294 | 20250 | 0.0 | - |
| 9.5529 | 20300 | 0.0033 | - |
| 9.5765 | 20350 | 0.0 | - |
| 9.6 | 20400 | 0.0 | - |
| 9.6235 | 20450 | 0.0 | - |
| 9.6471 | 20500 | 0.0 | - |
| 9.6706 | 20550 | 0.0035 | - |
| 9.6941 | 20600 | 0.0 | - |
| 9.7176 | 20650 | 0.0036 | - |
| 9.7412 | 20700 | 0.0035 | - |
| 9.7647 | 20750 | 0.0 | - |
| 9.7882 | 20800 | 0.0 | - |
| 9.8118 | 20850 | 0.0 | - |
| 9.8353 | 20900 | 0.0 | - |
| 9.8588 | 20950 | 0.0 | - |
| 9.8824 | 21000 | 0.0036 | - |
| 9.9059 | 21050 | 0.0127 | - |
| 9.9294 | 21100 | 0.0364 | - |
| 9.9529 | 21150 | 0.0 | - |
| 9.9765 | 21200 | 0.0 | - |
| 10.0 | 21250 | 0.0 | 0.1803 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.1.0
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 3.0.0
- 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}
}
```