---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: We will also discuss our deep concerns with actions by China, including in
Xinjiang, Hong Kong, Taiwan, cyber attacks on the United States, economic coercion
toward our allies.
- text: In the field of bilateral trade and investment, we have agreed that much can
be done to expand the present level of activity.
- text: We cannot allow the world's leading sponsor of terrorism to possess the planet's
most dangerous weapons.
- text: Because I do think this is not a function of whatever happened in Syria, I
think this is a function of the sanctions.
- text: One is to fight inflation, which has been hanging over our head and putting
a burden on the working people of this country for the last 10 years.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
---
# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | - 'We in the United States believe if we can promote democracy around the world, there will be more peace.'
- 'We recognise the transformative power of technology, including digital public infrastructure, to support sustainable development in the Indo-Pacific and deliver economic and social benefits.'
- 'This program strengthens democracy, transparency, and the rule of law in developing nations, and I ask you to fully fund this important initiative.'
|
| 1 | - 'I do not ever want to ever fight a war that is unconstitutional and I am the dangerous person.'
- "And so, we are at a moment where I really think threats to our democracy, threats to our core freedoms are very much on people's minds."
- 'My views in opposition to the cancellation of the war debt are a matter of detailed record in many public statements and in a recent message to the Congress.'
|
## 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("setfit_model_id")
# Run inference
preds = model("We cannot allow the world's leading sponsor of terrorism to possess the planet's most dangerous weapons.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 23.4393 | 46 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 486 |
| 1 | 486 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (1.003444469523018e-06, 1.003444469523018e-06)
- 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: 37
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.3295 | - |
| 0.0017 | 50 | 0.3132 | - |
| 0.0034 | 100 | 0.274 | - |
| 0.0051 | 150 | 0.2774 | - |
| 0.0068 | 200 | 0.2578 | - |
| 0.0084 | 250 | 0.2536 | - |
| 0.0101 | 300 | 0.3353 | - |
| 0.0118 | 350 | 0.253 | - |
| 0.0135 | 400 | 0.2865 | - |
| 0.0152 | 450 | 0.2894 | - |
| 0.0169 | 500 | 0.2554 | 0.2632 |
| 0.0186 | 550 | 0.2487 | - |
| 0.0203 | 600 | 0.2713 | - |
| 0.0220 | 650 | 0.2841 | - |
| 0.0237 | 700 | 0.2251 | - |
| 0.0253 | 750 | 0.2534 | - |
| 0.0270 | 800 | 0.2489 | - |
| 0.0287 | 850 | 0.2297 | - |
| 0.0304 | 900 | 0.2288 | - |
| 0.0321 | 950 | 0.211 | - |
| 0.0338 | 1000 | 0.188 | 0.2073 |
| 0.0355 | 1050 | 0.1488 | - |
| 0.0372 | 1100 | 0.2103 | - |
| 0.0389 | 1150 | 0.1607 | - |
| 0.0406 | 1200 | 0.0793 | - |
| 0.0422 | 1250 | 0.0968 | - |
| 0.0439 | 1300 | 0.0987 | - |
| 0.0456 | 1350 | 0.0786 | - |
| 0.0473 | 1400 | 0.0267 | - |
| 0.0490 | 1450 | 0.0432 | - |
| 0.0507 | 1500 | 0.0262 | 0.064 |
| 0.0524 | 1550 | 0.1269 | - |
| 0.0541 | 1600 | 0.039 | - |
| 0.0558 | 1650 | 0.0266 | - |
| 0.0575 | 1700 | 0.0455 | - |
| 0.0591 | 1750 | 0.0175 | - |
| 0.0608 | 1800 | 0.0157 | - |
| 0.0625 | 1850 | 0.0063 | - |
| 0.0642 | 1900 | 0.0146 | - |
| 0.0659 | 1950 | 0.0046 | - |
| **0.0676** | **2000** | **0.0046** | **0.0464** |
| 0.0693 | 2050 | 0.0035 | - |
| 0.0710 | 2100 | 0.0073 | - |
| 0.0727 | 2150 | 0.0012 | - |
| 0.0744 | 2200 | 0.0025 | - |
| 0.0760 | 2250 | 0.0023 | - |
| 0.0777 | 2300 | 0.0017 | - |
| 0.0794 | 2350 | 0.0012 | - |
| 0.0811 | 2400 | 0.0017 | - |
| 0.0828 | 2450 | 0.0016 | - |
| 0.0845 | 2500 | 0.0014 | 0.0535 |
| 0.0862 | 2550 | 0.0011 | - |
| 0.0879 | 2600 | 0.0021 | - |
| 0.0896 | 2650 | 0.0009 | - |
| 0.0913 | 2700 | 0.0008 | - |
| 0.0929 | 2750 | 0.0006 | - |
| 0.0946 | 2800 | 0.0007 | - |
| 0.0963 | 2850 | 0.0012 | - |
| 0.0980 | 2900 | 0.001 | - |
| 0.0997 | 2950 | 0.0005 | - |
| 0.1014 | 3000 | 0.0006 | 0.0575 |
| 0.1031 | 3050 | 0.0006 | - |
| 0.1048 | 3100 | 0.0004 | - |
| 0.1065 | 3150 | 0.0006 | - |
| 0.1082 | 3200 | 0.0005 | - |
| 0.1098 | 3250 | 0.0006 | - |
| 0.1115 | 3300 | 0.0005 | - |
| 0.1132 | 3350 | 0.0008 | - |
| 0.1149 | 3400 | 0.0003 | - |
| 0.1166 | 3450 | 0.0005 | - |
| 0.1183 | 3500 | 0.0004 | 0.0642 |
| 0.1200 | 3550 | 0.0006 | - |
| 0.1217 | 3600 | 0.0003 | - |
| 0.1234 | 3650 | 0.0009 | - |
| 0.1251 | 3700 | 0.0002 | - |
| 0.1267 | 3750 | 0.0003 | - |
| 0.1284 | 3800 | 0.0005 | - |
| 0.1301 | 3850 | 0.0002 | - |
| 0.1318 | 3900 | 0.0002 | - |
| 0.1335 | 3950 | 0.0005 | - |
| 0.1352 | 4000 | 0.0003 | 0.0697 |
| 0.1369 | 4050 | 0.0002 | - |
| 0.1386 | 4100 | 0.0002 | - |
| 0.1403 | 4150 | 0.0004 | - |
| 0.1420 | 4200 | 0.0012 | - |
| 0.1436 | 4250 | 0.0002 | - |
| 0.1453 | 4300 | 0.0002 | - |
| 0.1470 | 4350 | 0.0001 | - |
| 0.1487 | 4400 | 0.0002 | - |
| 0.1504 | 4450 | 0.0002 | - |
| 0.1521 | 4500 | 0.0003 | 0.0718 |
| 0.1538 | 4550 | 0.0003 | - |
| 0.1555 | 4600 | 0.0002 | - |
| 0.1572 | 4650 | 0.0002 | - |
| 0.1589 | 4700 | 0.0003 | - |
| 0.1605 | 4750 | 0.0002 | - |
| 0.1622 | 4800 | 0.0002 | - |
| 0.1639 | 4850 | 0.0002 | - |
| 0.1656 | 4900 | 0.0002 | - |
| 0.1673 | 4950 | 0.0002 | - |
| 0.1690 | 5000 | 0.0002 | 0.0684 |
| 0.1707 | 5050 | 0.0002 | - |
| 0.1724 | 5100 | 0.0002 | - |
| 0.1741 | 5150 | 0.0002 | - |
| 0.1758 | 5200 | 0.0003 | - |
| 0.1774 | 5250 | 0.0002 | - |
| 0.1791 | 5300 | 0.0001 | - |
| 0.1808 | 5350 | 0.0002 | - |
| 0.1825 | 5400 | 0.0001 | - |
| 0.1842 | 5450 | 0.0001 | - |
| 0.1859 | 5500 | 0.0001 | 0.0731 |
| 0.1876 | 5550 | 0.0002 | - |
| 0.1893 | 5600 | 0.0002 | - |
| 0.1910 | 5650 | 0.0001 | - |
| 0.1927 | 5700 | 0.0001 | - |
| 0.1943 | 5750 | 0.0001 | - |
| 0.1960 | 5800 | 0.0002 | - |
| 0.1977 | 5850 | 0.0001 | - |
| 0.1994 | 5900 | 0.0003 | - |
| 0.2011 | 5950 | 0.0002 | - |
| 0.2028 | 6000 | 0.0002 | 0.0724 |
| 0.2045 | 6050 | 0.0001 | - |
| 0.2062 | 6100 | 0.0001 | - |
| 0.2079 | 6150 | 0.0001 | - |
| 0.2096 | 6200 | 0.0001 | - |
| 0.2112 | 6250 | 0.0001 | - |
| 0.2129 | 6300 | 0.0002 | - |
| 0.2146 | 6350 | 0.0001 | - |
| 0.2163 | 6400 | 0.0001 | - |
| 0.2180 | 6450 | 0.0001 | - |
| 0.2197 | 6500 | 0.0001 | 0.0784 |
| 0.2214 | 6550 | 0.0001 | - |
| 0.2231 | 6600 | 0.0001 | - |
| 0.2248 | 6650 | 0.0001 | - |
| 0.2265 | 6700 | 0.0001 | - |
| 0.2281 | 6750 | 0.0001 | - |
| 0.2298 | 6800 | 0.0001 | - |
| 0.2315 | 6850 | 0.0001 | - |
| 0.2332 | 6900 | 0.0001 | - |
| 0.2349 | 6950 | 0.0002 | - |
| 0.2366 | 7000 | 0.0001 | 0.0672 |
| 0.2383 | 7050 | 0.0001 | - |
| 0.2400 | 7100 | 0.0001 | - |
| 0.2417 | 7150 | 0.0001 | - |
| 0.2434 | 7200 | 0.0001 | - |
| 0.2450 | 7250 | 0.0001 | - |
| 0.2467 | 7300 | 0.0001 | - |
| 0.2484 | 7350 | 0.0001 | - |
| 0.2501 | 7400 | 0.0001 | - |
| 0.2518 | 7450 | 0.0001 | - |
| 0.2535 | 7500 | 0.0001 | 0.0627 |
| 0.2552 | 7550 | 0.0001 | - |
| 0.2569 | 7600 | 0.0001 | - |
| 0.2586 | 7650 | 0.0 | - |
| 0.2603 | 7700 | 0.0001 | - |
| 0.2619 | 7750 | 0.0 | - |
| 0.2636 | 7800 | 0.0001 | - |
| 0.2653 | 7850 | 0.0001 | - |
| 0.2670 | 7900 | 0.0001 | - |
| 0.2687 | 7950 | 0.0001 | - |
| 0.2704 | 8000 | 0.0 | 0.0754 |
| 0.2721 | 8050 | 0.0001 | - |
| 0.2738 | 8100 | 0.0001 | - |
| 0.2755 | 8150 | 0.0 | - |
| 0.2772 | 8200 | 0.0 | - |
| 0.2788 | 8250 | 0.0 | - |
| 0.2805 | 8300 | 0.0001 | - |
| 0.2822 | 8350 | 0.0001 | - |
| 0.2839 | 8400 | 0.0001 | - |
| 0.2856 | 8450 | 0.0 | - |
| 0.2873 | 8500 | 0.0 | 0.0748 |
| 0.2890 | 8550 | 0.0 | - |
| 0.2907 | 8600 | 0.0 | - |
| 0.2924 | 8650 | 0.0 | - |
| 0.2941 | 8700 | 0.0 | - |
| 0.2957 | 8750 | 0.0001 | - |
| 0.2974 | 8800 | 0.0001 | - |
| 0.2991 | 8850 | 0.0001 | - |
| 0.3008 | 8900 | 0.0 | - |
| 0.3025 | 8950 | 0.0001 | - |
| 0.3042 | 9000 | 0.0001 | 0.057 |
| 0.3059 | 9050 | 0.0 | - |
| 0.3076 | 9100 | 0.0 | - |
| 0.3093 | 9150 | 0.0002 | - |
| 0.3110 | 9200 | 0.0 | - |
| 0.3126 | 9250 | 0.0 | - |
| 0.3143 | 9300 | 0.0 | - |
| 0.3160 | 9350 | 0.0001 | - |
| 0.3177 | 9400 | 0.0002 | - |
| 0.3194 | 9450 | 0.0 | - |
| 0.3211 | 9500 | 0.0 | 0.0781 |
| 0.3228 | 9550 | 0.0 | - |
| 0.3245 | 9600 | 0.0 | - |
| 0.3262 | 9650 | 0.0 | - |
| 0.3279 | 9700 | 0.0 | - |
| 0.3295 | 9750 | 0.0 | - |
| 0.3312 | 9800 | 0.0 | - |
| 0.3329 | 9850 | 0.0 | - |
| 0.3346 | 9900 | 0.0001 | - |
| 0.3363 | 9950 | 0.0 | - |
| 0.3380 | 10000 | 0.0 | 0.0698 |
| 0.3397 | 10050 | 0.0 | - |
| 0.3414 | 10100 | 0.0 | - |
| 0.3431 | 10150 | 0.0 | - |
| 0.3448 | 10200 | 0.0 | - |
| 0.3464 | 10250 | 0.0022 | - |
| 0.3481 | 10300 | 0.0 | - |
| 0.3498 | 10350 | 0.0001 | - |
| 0.3515 | 10400 | 0.0 | - |
| 0.3532 | 10450 | 0.0 | - |
| 0.3549 | 10500 | 0.0 | 0.0698 |
| 0.3566 | 10550 | 0.0 | - |
| 0.3583 | 10600 | 0.0 | - |
| 0.3600 | 10650 | 0.0 | - |
| 0.3617 | 10700 | 0.0 | - |
| 0.3633 | 10750 | 0.0 | - |
| 0.3650 | 10800 | 0.0 | - |
| 0.3667 | 10850 | 0.0 | - |
| 0.3684 | 10900 | 0.0001 | - |
| 0.3701 | 10950 | 0.0 | - |
| 0.3718 | 11000 | 0.0 | 0.0746 |
| 0.3735 | 11050 | 0.0 | - |
| 0.3752 | 11100 | 0.0 | - |
| 0.3769 | 11150 | 0.0001 | - |
| 0.3786 | 11200 | 0.0 | - |
| 0.3802 | 11250 | 0.0 | - |
| 0.3819 | 11300 | 0.0 | - |
| 0.3836 | 11350 | 0.0 | - |
| 0.3853 | 11400 | 0.0 | - |
| 0.3870 | 11450 | 0.0 | - |
| 0.3887 | 11500 | 0.0 | 0.0753 |
| 0.3904 | 11550 | 0.0 | - |
| 0.3921 | 11600 | 0.0001 | - |
| 0.3938 | 11650 | 0.0 | - |
| 0.3955 | 11700 | 0.0 | - |
| 0.3971 | 11750 | 0.0 | - |
| 0.3988 | 11800 | 0.0 | - |
| 0.4005 | 11850 | 0.0 | - |
| 0.4022 | 11900 | 0.0 | - |
| 0.4039 | 11950 | 0.0 | - |
| 0.4056 | 12000 | 0.0 | 0.0743 |
| 0.4073 | 12050 | 0.0 | - |
| 0.4090 | 12100 | 0.0 | - |
| 0.4107 | 12150 | 0.0 | - |
| 0.4124 | 12200 | 0.0 | - |
| 0.4140 | 12250 | 0.0 | - |
| 0.4157 | 12300 | 0.0 | - |
| 0.4174 | 12350 | 0.0 | - |
| 0.4191 | 12400 | 0.0 | - |
| 0.4208 | 12450 | 0.0 | - |
| 0.4225 | 12500 | 0.0 | 0.0733 |
| 0.4242 | 12550 | 0.0 | - |
| 0.4259 | 12600 | 0.0 | - |
| 0.4276 | 12650 | 0.0 | - |
| 0.4293 | 12700 | 0.0 | - |
| 0.4309 | 12750 | 0.0 | - |
| 0.4326 | 12800 | 0.0 | - |
| 0.4343 | 12850 | 0.0 | - |
| 0.4360 | 12900 | 0.0 | - |
| 0.4377 | 12950 | 0.0 | - |
| 0.4394 | 13000 | 0.0 | 0.072 |
| 0.4411 | 13050 | 0.0 | - |
| 0.4428 | 13100 | 0.0 | - |
| 0.4445 | 13150 | 0.0 | - |
| 0.4462 | 13200 | 0.0 | - |
| 0.4478 | 13250 | 0.0 | - |
| 0.4495 | 13300 | 0.0 | - |
| 0.4512 | 13350 | 0.0 | - |
| 0.4529 | 13400 | 0.0 | - |
| 0.4546 | 13450 | 0.0 | - |
| 0.4563 | 13500 | 0.0 | 0.0753 |
| 0.4580 | 13550 | 0.0 | - |
| 0.4597 | 13600 | 0.0 | - |
| 0.4614 | 13650 | 0.0 | - |
| 0.4631 | 13700 | 0.0 | - |
| 0.4647 | 13750 | 0.0 | - |
| 0.4664 | 13800 | 0.0 | - |
| 0.4681 | 13850 | 0.0 | - |
| 0.4698 | 13900 | 0.0 | - |
| 0.4715 | 13950 | 0.0 | - |
| 0.4732 | 14000 | 0.0 | 0.0756 |
| 0.4749 | 14050 | 0.0 | - |
| 0.4766 | 14100 | 0.0 | - |
| 0.4783 | 14150 | 0.0 | - |
| 0.4800 | 14200 | 0.0 | - |
| 0.4816 | 14250 | 0.0 | - |
| 0.4833 | 14300 | 0.0 | - |
| 0.4850 | 14350 | 0.0 | - |
| 0.4867 | 14400 | 0.0 | - |
| 0.4884 | 14450 | 0.0 | - |
| 0.4901 | 14500 | 0.0 | 0.0622 |
| 0.4918 | 14550 | 0.0 | - |
| 0.4935 | 14600 | 0.0 | - |
| 0.4952 | 14650 | 0.0 | - |
| 0.4969 | 14700 | 0.0 | - |
| 0.4985 | 14750 | 0.0 | - |
| 0.5002 | 14800 | 0.0 | - |
| 0.5019 | 14850 | 0.0 | - |
| 0.5036 | 14900 | 0.0 | - |
| 0.5053 | 14950 | 0.0 | - |
| 0.5070 | 15000 | 0.0 | 0.0676 |
| 0.5087 | 15050 | 0.0 | - |
| 0.5104 | 15100 | 0.0 | - |
| 0.5121 | 15150 | 0.0 | - |
| 0.5138 | 15200 | 0.0 | - |
| 0.5154 | 15250 | 0.0 | - |
| 0.5171 | 15300 | 0.0 | - |
| 0.5188 | 15350 | 0.0 | - |
| 0.5205 | 15400 | 0.0 | - |
| 0.5222 | 15450 | 0.0 | - |
| 0.5239 | 15500 | 0.0 | 0.0668 |
| 0.5256 | 15550 | 0.0 | - |
| 0.5273 | 15600 | 0.0 | - |
| 0.5290 | 15650 | 0.0 | - |
| 0.5307 | 15700 | 0.0 | - |
| 0.5323 | 15750 | 0.0 | - |
| 0.5340 | 15800 | 0.0 | - |
| 0.5357 | 15850 | 0.0 | - |
| 0.5374 | 15900 | 0.0 | - |
| 0.5391 | 15950 | 0.0 | - |
| 0.5408 | 16000 | 0.0 | 0.0707 |
| 0.5425 | 16050 | 0.0 | - |
| 0.5442 | 16100 | 0.0 | - |
| 0.5459 | 16150 | 0.0 | - |
| 0.5476 | 16200 | 0.0 | - |
| 0.5492 | 16250 | 0.0 | - |
| 0.5509 | 16300 | 0.0 | - |
| 0.5526 | 16350 | 0.0 | - |
| 0.5543 | 16400 | 0.0 | - |
| 0.5560 | 16450 | 0.0 | - |
| 0.5577 | 16500 | 0.0 | 0.0644 |
| 0.5594 | 16550 | 0.0 | - |
| 0.5611 | 16600 | 0.0 | - |
| 0.5628 | 16650 | 0.0 | - |
| 0.5645 | 16700 | 0.0 | - |
| 0.5661 | 16750 | 0.0 | - |
| 0.5678 | 16800 | 0.0 | - |
| 0.5695 | 16850 | 0.0 | - |
| 0.5712 | 16900 | 0.0 | - |
| 0.5729 | 16950 | 0.0 | - |
| 0.5746 | 17000 | 0.0 | 0.0742 |
| 0.5763 | 17050 | 0.0 | - |
| 0.5780 | 17100 | 0.0 | - |
| 0.5797 | 17150 | 0.0 | - |
| 0.5814 | 17200 | 0.0 | - |
| 0.5830 | 17250 | 0.0 | - |
| 0.5847 | 17300 | 0.0 | - |
| 0.5864 | 17350 | 0.0 | - |
| 0.5881 | 17400 | 0.0 | - |
| 0.5898 | 17450 | 0.0 | - |
| 0.5915 | 17500 | 0.0 | 0.0738 |
| 0.5932 | 17550 | 0.0 | - |
| 0.5949 | 17600 | 0.0 | - |
| 0.5966 | 17650 | 0.0 | - |
| 0.5983 | 17700 | 0.0 | - |
| 0.5999 | 17750 | 0.0 | - |
| 0.6016 | 17800 | 0.0 | - |
| 0.6033 | 17850 | 0.0 | - |
| 0.6050 | 17900 | 0.0 | - |
| 0.6067 | 17950 | 0.0 | - |
| 0.6084 | 18000 | 0.0 | 0.0725 |
| 0.6101 | 18050 | 0.0 | - |
| 0.6118 | 18100 | 0.0 | - |
| 0.6135 | 18150 | 0.0 | - |
| 0.6152 | 18200 | 0.0 | - |
| 0.6168 | 18250 | 0.0 | - |
| 0.6185 | 18300 | 0.0 | - |
| 0.6202 | 18350 | 0.0 | - |
| 0.6219 | 18400 | 0.0 | - |
| 0.6236 | 18450 | 0.0 | - |
| 0.6253 | 18500 | 0.0 | 0.0724 |
| 0.6270 | 18550 | 0.0 | - |
| 0.6287 | 18600 | 0.0 | - |
| 0.6304 | 18650 | 0.0 | - |
| 0.6321 | 18700 | 0.0 | - |
| 0.6337 | 18750 | 0.0 | - |
| 0.6354 | 18800 | 0.0 | - |
| 0.6371 | 18850 | 0.0 | - |
| 0.6388 | 18900 | 0.0 | - |
| 0.6405 | 18950 | 0.0 | - |
| 0.6422 | 19000 | 0.0 | 0.0622 |
| 0.6439 | 19050 | 0.0 | - |
| 0.6456 | 19100 | 0.0 | - |
| 0.6473 | 19150 | 0.0 | - |
| 0.6490 | 19200 | 0.0 | - |
| 0.6506 | 19250 | 0.0 | - |
| 0.6523 | 19300 | 0.0 | - |
| 0.6540 | 19350 | 0.0 | - |
| 0.6557 | 19400 | 0.0 | - |
| 0.6574 | 19450 | 0.0 | - |
| 0.6591 | 19500 | 0.0 | 0.0754 |
| 0.6608 | 19550 | 0.0 | - |
| 0.6625 | 19600 | 0.0 | - |
| 0.6642 | 19650 | 0.0 | - |
| 0.6659 | 19700 | 0.0 | - |
| 0.6675 | 19750 | 0.0 | - |
| 0.6692 | 19800 | 0.0 | - |
| 0.6709 | 19850 | 0.0 | - |
| 0.6726 | 19900 | 0.0 | - |
| 0.6743 | 19950 | 0.0 | - |
| 0.6760 | 20000 | 0.0 | 0.0723 |
| 0.6777 | 20050 | 0.0 | - |
| 0.6794 | 20100 | 0.0 | - |
| 0.6811 | 20150 | 0.0 | - |
| 0.6828 | 20200 | 0.0 | - |
| 0.6844 | 20250 | 0.0 | - |
| 0.6861 | 20300 | 0.0 | - |
| 0.6878 | 20350 | 0.0 | - |
| 0.6895 | 20400 | 0.0 | - |
| 0.6912 | 20450 | 0.0 | - |
| 0.6929 | 20500 | 0.0 | 0.0741 |
| 0.6946 | 20550 | 0.0 | - |
| 0.6963 | 20600 | 0.0 | - |
| 0.6980 | 20650 | 0.0 | - |
| 0.6997 | 20700 | 0.0 | - |
| 0.7013 | 20750 | 0.0 | - |
| 0.7030 | 20800 | 0.0 | - |
| 0.7047 | 20850 | 0.0 | - |
| 0.7064 | 20900 | 0.0 | - |
| 0.7081 | 20950 | 0.0 | - |
| 0.7098 | 21000 | 0.0 | 0.0733 |
| 0.7115 | 21050 | 0.0 | - |
| 0.7132 | 21100 | 0.0 | - |
| 0.7149 | 21150 | 0.0 | - |
| 0.7166 | 21200 | 0.0 | - |
| 0.7182 | 21250 | 0.0 | - |
| 0.7199 | 21300 | 0.0 | - |
| 0.7216 | 21350 | 0.0 | - |
| 0.7233 | 21400 | 0.0 | - |
| 0.7250 | 21450 | 0.0 | - |
| 0.7267 | 21500 | 0.0 | 0.0757 |
| 0.7284 | 21550 | 0.0 | - |
| 0.7301 | 21600 | 0.0 | - |
| 0.7318 | 21650 | 0.0 | - |
| 0.7335 | 21700 | 0.0 | - |
| 0.7351 | 21750 | 0.0 | - |
| 0.7368 | 21800 | 0.0 | - |
| 0.7385 | 21850 | 0.0 | - |
| 0.7402 | 21900 | 0.0 | - |
| 0.7419 | 21950 | 0.0 | - |
| 0.7436 | 22000 | 0.0 | 0.0766 |
| 0.7453 | 22050 | 0.0 | - |
| 0.7470 | 22100 | 0.0 | - |
| 0.7487 | 22150 | 0.0 | - |
| 0.7504 | 22200 | 0.0 | - |
| 0.7520 | 22250 | 0.0 | - |
| 0.7537 | 22300 | 0.0 | - |
| 0.7554 | 22350 | 0.0 | - |
| 0.7571 | 22400 | 0.0 | - |
| 0.7588 | 22450 | 0.0 | - |
| 0.7605 | 22500 | 0.0 | 0.0757 |
| 0.7622 | 22550 | 0.0 | - |
| 0.7639 | 22600 | 0.0 | - |
| 0.7656 | 22650 | 0.0 | - |
| 0.7673 | 22700 | 0.0 | - |
| 0.7689 | 22750 | 0.0 | - |
| 0.7706 | 22800 | 0.0 | - |
| 0.7723 | 22850 | 0.0 | - |
| 0.7740 | 22900 | 0.0 | - |
| 0.7757 | 22950 | 0.0 | - |
| 0.7774 | 23000 | 0.0 | 0.0755 |
| 0.7791 | 23050 | 0.0 | - |
| 0.7808 | 23100 | 0.0 | - |
| 0.7825 | 23150 | 0.0 | - |
| 0.7842 | 23200 | 0.0 | - |
| 0.7858 | 23250 | 0.0 | - |
| 0.7875 | 23300 | 0.0 | - |
| 0.7892 | 23350 | 0.0 | - |
| 0.7909 | 23400 | 0.0 | - |
| 0.7926 | 23450 | 0.0 | - |
| 0.7943 | 23500 | 0.0 | 0.076 |
| 0.7960 | 23550 | 0.0 | - |
| 0.7977 | 23600 | 0.0 | - |
| 0.7994 | 23650 | 0.0 | - |
| 0.8011 | 23700 | 0.0 | - |
| 0.8027 | 23750 | 0.0 | - |
| 0.8044 | 23800 | 0.0 | - |
| 0.8061 | 23850 | 0.0 | - |
| 0.8078 | 23900 | 0.0 | - |
| 0.8095 | 23950 | 0.0 | - |
| 0.8112 | 24000 | 0.0 | 0.0756 |
| 0.8129 | 24050 | 0.0 | - |
| 0.8146 | 24100 | 0.0 | - |
| 0.8163 | 24150 | 0.0 | - |
| 0.8180 | 24200 | 0.0 | - |
| 0.8196 | 24250 | 0.0 | - |
| 0.8213 | 24300 | 0.0 | - |
| 0.8230 | 24350 | 0.0 | - |
| 0.8247 | 24400 | 0.0 | - |
| 0.8264 | 24450 | 0.0 | - |
| 0.8281 | 24500 | 0.0 | 0.0759 |
| 0.8298 | 24550 | 0.0 | - |
| 0.8315 | 24600 | 0.0 | - |
| 0.8332 | 24650 | 0.0 | - |
| 0.8349 | 24700 | 0.0 | - |
| 0.8365 | 24750 | 0.0 | - |
| 0.8382 | 24800 | 0.0 | - |
| 0.8399 | 24850 | 0.0 | - |
| 0.8416 | 24900 | 0.0 | - |
| 0.8433 | 24950 | 0.0 | - |
| 0.8450 | 25000 | 0.0 | 0.0762 |
| 0.8467 | 25050 | 0.0 | - |
| 0.8484 | 25100 | 0.0 | - |
| 0.8501 | 25150 | 0.0 | - |
| 0.8518 | 25200 | 0.0 | - |
| 0.8534 | 25250 | 0.0 | - |
| 0.8551 | 25300 | 0.0 | - |
| 0.8568 | 25350 | 0.0 | - |
| 0.8585 | 25400 | 0.0 | - |
| 0.8602 | 25450 | 0.0 | - |
| 0.8619 | 25500 | 0.0 | 0.0733 |
| 0.8636 | 25550 | 0.0 | - |
| 0.8653 | 25600 | 0.0 | - |
| 0.8670 | 25650 | 0.0 | - |
| 0.8687 | 25700 | 0.0 | - |
| 0.8703 | 25750 | 0.0 | - |
| 0.8720 | 25800 | 0.0 | - |
| 0.8737 | 25850 | 0.0 | - |
| 0.8754 | 25900 | 0.0 | - |
| 0.8771 | 25950 | 0.0 | - |
| 0.8788 | 26000 | 0.0 | 0.0742 |
| 0.8805 | 26050 | 0.0 | - |
| 0.8822 | 26100 | 0.0 | - |
| 0.8839 | 26150 | 0.0 | - |
| 0.8856 | 26200 | 0.0 | - |
| 0.8872 | 26250 | 0.0 | - |
| 0.8889 | 26300 | 0.0 | - |
| 0.8906 | 26350 | 0.0 | - |
| 0.8923 | 26400 | 0.0 | - |
| 0.8940 | 26450 | 0.0 | - |
| 0.8957 | 26500 | 0.0 | 0.0756 |
| 0.8974 | 26550 | 0.0 | - |
| 0.8991 | 26600 | 0.0 | - |
| 0.9008 | 26650 | 0.0 | - |
| 0.9025 | 26700 | 0.0 | - |
| 0.9041 | 26750 | 0.0 | - |
| 0.9058 | 26800 | 0.0 | - |
| 0.9075 | 26850 | 0.0 | - |
| 0.9092 | 26900 | 0.0 | - |
| 0.9109 | 26950 | 0.0 | - |
| 0.9126 | 27000 | 0.0 | 0.0751 |
| 0.9143 | 27050 | 0.0 | - |
| 0.9160 | 27100 | 0.0 | - |
| 0.9177 | 27150 | 0.0 | - |
| 0.9194 | 27200 | 0.0 | - |
| 0.9210 | 27250 | 0.0 | - |
| 0.9227 | 27300 | 0.0 | - |
| 0.9244 | 27350 | 0.0 | - |
| 0.9261 | 27400 | 0.0 | - |
| 0.9278 | 27450 | 0.0 | - |
| 0.9295 | 27500 | 0.0 | 0.075 |
| 0.9312 | 27550 | 0.0 | - |
| 0.9329 | 27600 | 0.0 | - |
| 0.9346 | 27650 | 0.0 | - |
| 0.9363 | 27700 | 0.0 | - |
| 0.9379 | 27750 | 0.0 | - |
| 0.9396 | 27800 | 0.0 | - |
| 0.9413 | 27850 | 0.0 | - |
| 0.9430 | 27900 | 0.0 | - |
| 0.9447 | 27950 | 0.0 | - |
| 0.9464 | 28000 | 0.0 | 0.0725 |
| 0.9481 | 28050 | 0.0 | - |
| 0.9498 | 28100 | 0.0 | - |
| 0.9515 | 28150 | 0.0 | - |
| 0.9532 | 28200 | 0.0 | - |
| 0.9548 | 28250 | 0.0 | - |
| 0.9565 | 28300 | 0.0 | - |
| 0.9582 | 28350 | 0.0 | - |
| 0.9599 | 28400 | 0.0 | - |
| 0.9616 | 28450 | 0.0 | - |
| 0.9633 | 28500 | 0.0 | 0.0761 |
| 0.9650 | 28550 | 0.0 | - |
| 0.9667 | 28600 | 0.0 | - |
| 0.9684 | 28650 | 0.0 | - |
| 0.9701 | 28700 | 0.0 | - |
| 0.9717 | 28750 | 0.0 | - |
| 0.9734 | 28800 | 0.0 | - |
| 0.9751 | 28850 | 0.0 | - |
| 0.9768 | 28900 | 0.0 | - |
| 0.9785 | 28950 | 0.0 | - |
| 0.9802 | 29000 | 0.0 | 0.0759 |
| 0.9819 | 29050 | 0.0 | - |
| 0.9836 | 29100 | 0.0 | - |
| 0.9853 | 29150 | 0.0 | - |
| 0.9870 | 29200 | 0.0 | - |
| 0.9886 | 29250 | 0.0 | - |
| 0.9903 | 29300 | 0.0 | - |
| 0.9920 | 29350 | 0.0 | - |
| 0.9937 | 29400 | 0.0 | - |
| 0.9954 | 29450 | 0.0 | - |
| 0.9971 | 29500 | 0.0 | 0.0761 |
| 0.9988 | 29550 | 0.0 | - |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.11
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.25.1
- PyTorch: 2.1.2
- Datasets: 2.15.0
- Tokenizers: 0.13.3
## 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}
}
```