---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-mpnet-base-v2
metrics:
- accuracy
widget:
- text: Needs Power and Mouse Cable to Plug in:Needs Power and Mouse Cable to Plug
in back instead of side, In the way of operating a mouse in small area.
- text: wireless router via built-in wireless took no time:Connecting to my wireless
router via built-in wireless took no time at all.
- text: The battery life is probably an:The battery life is probably an hour at best.
- text: and with free shipping and no tax:The 13" Macbook Pro just fits in my budget
and with free shipping and no tax to CA this is the best price we can get for
a great product.
- text: product is top quality.:The price was very good, and the product is top quality.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Polarity Model 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: 0.7788235294117647
name: Accuracy
---
# SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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. In particular, this model is in charge of classifying aspect polarities.
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.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. Use a SetFit model to filter these possible aspect span candidates.
3. **Use this SetFit model to classify the filtered aspect span candidates.**
## 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
- **spaCy Model:** en_core_web_sm
- **SetFitABSA Aspect Model:** [setfit-absa-aspect](https://huggingface.co/setfit-absa-aspect)
- **SetFitABSA Polarity Model:** [marcelomoreno26/all-mpnet-base-v2-absa-polarity2](https://huggingface.co/marcelomoreno26/all-mpnet-base-v2-absa-polarity2)
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 4 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 | - 'skip taking the cord with me because:I charge it at night and skip taking the cord with me because of the good battery life.'
- 'The tech guy then said the:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'
- 'all dark, power light steady, hard:\xa0One night I turned the freaking thing off after using it, the next day I turn it on, no GUI, screen all dark, power light steady, hard drive light steady and not flashing as it usually does.'
|
| positive | - 'of the good battery life.:I charge it at night and skip taking the cord with me because of the good battery life.'
- 'is of high quality, has a:it is of high quality, has a killer GUI, is extremely stable, is highly expandable, is bundled with lots of very good applications, is easy to use, and is absolutely gorgeous.'
- 'has a killer GUI, is extremely:it is of high quality, has a killer GUI, is extremely stable, is highly expandable, is bundled with lots of very good applications, is easy to use, and is absolutely gorgeous.'
|
| negative | - 'then said the service center does not do:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'
- 'concern to the "sales" team, which is:The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the "sales" team, which is the retail shop which I bought my netbook from.'
- 'on, no GUI, screen all:\xa0One night I turned the freaking thing off after using it, the next day I turn it on, no GUI, screen all dark, power light steady, hard drive light steady and not flashing as it usually does.'
|
| conflict | - '-No backlit keyboard, but not:-No backlit keyboard, but not an issue for me.'
- "to replace the battery once, but:I did have to replace the battery once, but that was only a couple months ago and it's been working perfect ever since."
- 'Yes, they cost more, but:Yes, they cost more, but they more than make up for it in speed, construction quality, and longevity.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7788 |
## 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 AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"setfit-absa-aspect",
"marcelomoreno26/all-mpnet-base-v2-absa-polarity2",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 24.3447 | 80 |
| Label | Training Sample Count |
|:---------|:----------------------|
| negative | 235 |
| neutral | 127 |
| positive | 271 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.3333 | 1 | 0.3749 | - |
| 0.0030 | 50 | 0.3097 | - |
| 0.0059 | 100 | 0.2214 | - |
| 0.0089 | 150 | 0.2125 | - |
| 0.0119 | 200 | 0.3202 | - |
| 0.0148 | 250 | 0.1878 | - |
| 0.0178 | 300 | 0.1208 | - |
| 0.0208 | 350 | 0.2414 | - |
| 0.0237 | 400 | 0.1961 | - |
| 0.0267 | 450 | 0.0607 | - |
| 0.0296 | 500 | 0.1103 | - |
| 0.0326 | 550 | 0.1213 | - |
| 0.0356 | 600 | 0.0972 | - |
| 0.0385 | 650 | 0.0124 | - |
| 0.0415 | 700 | 0.0151 | - |
| 0.0445 | 750 | 0.1517 | - |
| 0.0474 | 800 | 0.004 | - |
| 0.0504 | 850 | 0.0204 | - |
| 0.0534 | 900 | 0.0541 | - |
| 0.0563 | 950 | 0.003 | - |
| 0.0593 | 1000 | 0.0008 | - |
| 0.0623 | 1050 | 0.0703 | - |
| 0.0652 | 1100 | 0.0013 | - |
| 0.0682 | 1150 | 0.0007 | - |
| 0.0712 | 1200 | 0.0009 | - |
| 0.0741 | 1250 | 0.0004 | - |
| 0.0771 | 1300 | 0.0004 | - |
| 0.0801 | 1350 | 0.0005 | - |
| 0.0830 | 1400 | 0.0006 | - |
| 0.0860 | 1450 | 0.0004 | - |
| 0.0889 | 1500 | 0.0002 | - |
| 0.0919 | 1550 | 0.0002 | - |
| 0.0949 | 1600 | 0.0001 | - |
| 0.0978 | 1650 | 0.0006 | - |
| 0.1008 | 1700 | 0.0002 | - |
| 0.1038 | 1750 | 0.0012 | - |
| 0.1067 | 1800 | 0.0008 | - |
| 0.1097 | 1850 | 0.0048 | - |
| 0.1127 | 1900 | 0.0007 | - |
| 0.1156 | 1950 | 0.0001 | - |
| 0.1186 | 2000 | 0.0001 | - |
| 0.1216 | 2050 | 0.0001 | - |
| 0.1245 | 2100 | 0.0001 | - |
| 0.1275 | 2150 | 0.0001 | - |
| 0.1305 | 2200 | 0.0001 | - |
| 0.1334 | 2250 | 0.0 | - |
| 0.1364 | 2300 | 0.0001 | - |
| 0.1394 | 2350 | 0.0002 | - |
| 0.1423 | 2400 | 0.0 | - |
| 0.1453 | 2450 | 0.0 | - |
| 0.1482 | 2500 | 0.0589 | - |
| 0.1512 | 2550 | 0.0036 | - |
| 0.1542 | 2600 | 0.0013 | - |
| 0.1571 | 2650 | 0.0 | - |
| 0.1601 | 2700 | 0.0001 | - |
| 0.1631 | 2750 | 0.0004 | - |
| 0.1660 | 2800 | 0.0 | - |
| 0.1690 | 2850 | 0.0002 | - |
| 0.1720 | 2900 | 0.0096 | - |
| 0.1749 | 2950 | 0.0 | - |
| 0.1779 | 3000 | 0.0 | - |
| 0.1809 | 3050 | 0.0001 | - |
| 0.1838 | 3100 | 0.0 | - |
| 0.1868 | 3150 | 0.0001 | - |
| 0.1898 | 3200 | 0.0001 | - |
| 0.1927 | 3250 | 0.0 | - |
| 0.1957 | 3300 | 0.0 | - |
| 0.1986 | 3350 | 0.0001 | - |
| 0.2016 | 3400 | 0.0 | - |
| 0.2046 | 3450 | 0.0002 | - |
| 0.2075 | 3500 | 0.0 | - |
| 0.2105 | 3550 | 0.0 | - |
| 0.2135 | 3600 | 0.0001 | - |
| 0.2164 | 3650 | 0.0 | - |
| 0.2194 | 3700 | 0.0 | - |
| 0.2224 | 3750 | 0.0001 | - |
| 0.2253 | 3800 | 0.0 | - |
| 0.2283 | 3850 | 0.0 | - |
| 0.2313 | 3900 | 0.0 | - |
| 0.2342 | 3950 | 0.0 | - |
| 0.2372 | 4000 | 0.0 | - |
| 0.2402 | 4050 | 0.0 | - |
| 0.2431 | 4100 | 0.0 | - |
| 0.2461 | 4150 | 0.0 | - |
| 0.2491 | 4200 | 0.0 | - |
| 0.2520 | 4250 | 0.0 | - |
| 0.2550 | 4300 | 0.0 | - |
| 0.2579 | 4350 | 0.0 | - |
| 0.2609 | 4400 | 0.0 | - |
| 0.2639 | 4450 | 0.0 | - |
| 0.2668 | 4500 | 0.0 | - |
| 0.2698 | 4550 | 0.0 | - |
| 0.2728 | 4600 | 0.0 | - |
| 0.2757 | 4650 | 0.0 | - |
| 0.2787 | 4700 | 0.0 | - |
| 0.2817 | 4750 | 0.0 | - |
| 0.2846 | 4800 | 0.0 | - |
| 0.2876 | 4850 | 0.0001 | - |
| 0.2906 | 4900 | 0.0071 | - |
| 0.2935 | 4950 | 0.1151 | - |
| 0.2965 | 5000 | 0.0055 | - |
| 0.2995 | 5050 | 0.0005 | - |
| 0.3024 | 5100 | 0.0041 | - |
| 0.3054 | 5150 | 0.0001 | - |
| 0.3083 | 5200 | 0.0003 | - |
| 0.3113 | 5250 | 0.0001 | - |
| 0.3143 | 5300 | 0.0 | - |
| 0.3172 | 5350 | 0.0001 | - |
| 0.3202 | 5400 | 0.0 | - |
| 0.3232 | 5450 | 0.0 | - |
| 0.3261 | 5500 | 0.0 | - |
| 0.3291 | 5550 | 0.0 | - |
| 0.3321 | 5600 | 0.0 | - |
| 0.3350 | 5650 | 0.0 | - |
| 0.3380 | 5700 | 0.0 | - |
| 0.3410 | 5750 | 0.0 | - |
| 0.3439 | 5800 | 0.0 | - |
| 0.3469 | 5850 | 0.0 | - |
| 0.3499 | 5900 | 0.0 | - |
| 0.3528 | 5950 | 0.0 | - |
| 0.3558 | 6000 | 0.0 | - |
| 0.3588 | 6050 | 0.0 | - |
| 0.3617 | 6100 | 0.0 | - |
| 0.3647 | 6150 | 0.0 | - |
| 0.3676 | 6200 | 0.0 | - |
| 0.3706 | 6250 | 0.0 | - |
| 0.3736 | 6300 | 0.0 | - |
| 0.3765 | 6350 | 0.0 | - |
| 0.3795 | 6400 | 0.0 | - |
| 0.3825 | 6450 | 0.0 | - |
| 0.3854 | 6500 | 0.0 | - |
| 0.3884 | 6550 | 0.0 | - |
| 0.3914 | 6600 | 0.0 | - |
| 0.3943 | 6650 | 0.0 | - |
| 0.3973 | 6700 | 0.0 | - |
| 0.4003 | 6750 | 0.0 | - |
| 0.4032 | 6800 | 0.0 | - |
| 0.4062 | 6850 | 0.0 | - |
| 0.4092 | 6900 | 0.0 | - |
| 0.4121 | 6950 | 0.0 | - |
| 0.4151 | 7000 | 0.0 | - |
| 0.4181 | 7050 | 0.0 | - |
| 0.4210 | 7100 | 0.0 | - |
| 0.4240 | 7150 | 0.0 | - |
| 0.4269 | 7200 | 0.0 | - |
| 0.4299 | 7250 | 0.0 | - |
| 0.4329 | 7300 | 0.0 | - |
| 0.4358 | 7350 | 0.0 | - |
| 0.4388 | 7400 | 0.0 | - |
| 0.4418 | 7450 | 0.0 | - |
| 0.4447 | 7500 | 0.0 | - |
| 0.4477 | 7550 | 0.0 | - |
| 0.4507 | 7600 | 0.0 | - |
| 0.4536 | 7650 | 0.0003 | - |
| 0.4566 | 7700 | 0.0 | - |
| 0.4596 | 7750 | 0.0 | - |
| 0.4625 | 7800 | 0.0 | - |
| 0.4655 | 7850 | 0.0 | - |
| 0.4685 | 7900 | 0.0 | - |
| 0.4714 | 7950 | 0.0 | - |
| 0.4744 | 8000 | 0.0 | - |
| 0.4773 | 8050 | 0.0 | - |
| 0.4803 | 8100 | 0.0 | - |
| 0.4833 | 8150 | 0.0 | - |
| 0.4862 | 8200 | 0.0 | - |
| 0.4892 | 8250 | 0.0 | - |
| 0.4922 | 8300 | 0.0 | - |
| 0.4951 | 8350 | 0.0 | - |
| 0.4981 | 8400 | 0.0 | - |
| 0.5011 | 8450 | 0.0 | - |
| 0.5040 | 8500 | 0.0 | - |
| 0.5070 | 8550 | 0.0 | - |
| 0.5100 | 8600 | 0.0 | - |
| 0.5129 | 8650 | 0.0 | - |
| 0.5159 | 8700 | 0.0 | - |
| 0.5189 | 8750 | 0.0 | - |
| 0.5218 | 8800 | 0.0 | - |
| 0.5248 | 8850 | 0.0 | - |
| 0.5278 | 8900 | 0.0 | - |
| 0.5307 | 8950 | 0.0 | - |
| 0.5337 | 9000 | 0.0 | - |
| 0.5366 | 9050 | 0.0 | - |
| 0.5396 | 9100 | 0.0 | - |
| 0.5426 | 9150 | 0.0 | - |
| 0.5455 | 9200 | 0.0 | - |
| 0.5485 | 9250 | 0.0 | - |
| 0.5515 | 9300 | 0.0 | - |
| 0.5544 | 9350 | 0.0 | - |
| 0.5574 | 9400 | 0.0 | - |
| 0.5604 | 9450 | 0.0 | - |
| 0.5633 | 9500 | 0.0 | - |
| 0.5663 | 9550 | 0.0 | - |
| 0.5693 | 9600 | 0.0 | - |
| 0.5722 | 9650 | 0.0 | - |
| 0.5752 | 9700 | 0.0 | - |
| 0.5782 | 9750 | 0.0 | - |
| 0.5811 | 9800 | 0.0 | - |
| 0.5841 | 9850 | 0.0 | - |
| 0.5870 | 9900 | 0.0 | - |
| 0.5900 | 9950 | 0.0 | - |
| 0.5930 | 10000 | 0.0 | - |
| 0.5959 | 10050 | 0.0 | - |
| 0.5989 | 10100 | 0.0 | - |
| 0.6019 | 10150 | 0.0 | - |
| 0.6048 | 10200 | 0.0 | - |
| 0.6078 | 10250 | 0.0 | - |
| 0.6108 | 10300 | 0.0 | - |
| 0.6137 | 10350 | 0.0 | - |
| 0.6167 | 10400 | 0.0 | - |
| 0.6197 | 10450 | 0.0 | - |
| 0.6226 | 10500 | 0.0 | - |
| 0.6256 | 10550 | 0.0 | - |
| 0.6286 | 10600 | 0.0 | - |
| 0.6315 | 10650 | 0.0 | - |
| 0.6345 | 10700 | 0.0 | - |
| 0.6375 | 10750 | 0.0 | - |
| 0.6404 | 10800 | 0.0 | - |
| 0.6434 | 10850 | 0.0 | - |
| 0.6463 | 10900 | 0.0 | - |
| 0.6493 | 10950 | 0.0 | - |
| 0.6523 | 11000 | 0.0 | - |
| 0.6552 | 11050 | 0.0 | - |
| 0.6582 | 11100 | 0.0 | - |
| 0.6612 | 11150 | 0.0 | - |
| 0.6641 | 11200 | 0.0 | - |
| 0.6671 | 11250 | 0.0 | - |
| 0.6701 | 11300 | 0.0 | - |
| 0.6730 | 11350 | 0.0 | - |
| 0.6760 | 11400 | 0.0 | - |
| 0.6790 | 11450 | 0.0 | - |
| 0.6819 | 11500 | 0.0 | - |
| 0.6849 | 11550 | 0.0 | - |
| 0.6879 | 11600 | 0.0 | - |
| 0.6908 | 11650 | 0.0 | - |
| 0.6938 | 11700 | 0.0 | - |
| 0.6968 | 11750 | 0.0 | - |
| 0.6997 | 11800 | 0.0 | - |
| 0.7027 | 11850 | 0.0 | - |
| 0.7056 | 11900 | 0.0 | - |
| 0.7086 | 11950 | 0.0 | - |
| 0.7116 | 12000 | 0.0 | - |
| 0.7145 | 12050 | 0.0 | - |
| 0.7175 | 12100 | 0.0 | - |
| 0.7205 | 12150 | 0.0 | - |
| 0.7234 | 12200 | 0.0 | - |
| 0.7264 | 12250 | 0.0 | - |
| 0.7294 | 12300 | 0.0 | - |
| 0.7323 | 12350 | 0.0 | - |
| 0.7353 | 12400 | 0.0 | - |
| 0.7383 | 12450 | 0.0 | - |
| 0.7412 | 12500 | 0.0 | - |
| 0.7442 | 12550 | 0.0 | - |
| 0.7472 | 12600 | 0.0 | - |
| 0.7501 | 12650 | 0.0 | - |
| 0.7531 | 12700 | 0.0 | - |
| 0.7560 | 12750 | 0.0 | - |
| 0.7590 | 12800 | 0.0 | - |
| 0.7620 | 12850 | 0.0 | - |
| 0.7649 | 12900 | 0.0 | - |
| 0.7679 | 12950 | 0.0 | - |
| 0.7709 | 13000 | 0.0 | - |
| 0.7738 | 13050 | 0.0 | - |
| 0.7768 | 13100 | 0.0 | - |
| 0.7798 | 13150 | 0.0 | - |
| 0.7827 | 13200 | 0.0 | - |
| 0.7857 | 13250 | 0.0 | - |
| 0.7887 | 13300 | 0.0 | - |
| 0.7916 | 13350 | 0.0 | - |
| 0.7946 | 13400 | 0.0 | - |
| 0.7976 | 13450 | 0.0 | - |
| 0.8005 | 13500 | 0.0 | - |
| 0.8035 | 13550 | 0.0 | - |
| 0.8065 | 13600 | 0.0 | - |
| 0.8094 | 13650 | 0.0 | - |
| 0.8124 | 13700 | 0.0 | - |
| 0.8153 | 13750 | 0.0 | - |
| 0.8183 | 13800 | 0.0 | - |
| 0.8213 | 13850 | 0.0 | - |
| 0.8242 | 13900 | 0.0 | - |
| 0.8272 | 13950 | 0.0 | - |
| 0.8302 | 14000 | 0.0 | - |
| 0.8331 | 14050 | 0.0 | - |
| 0.8361 | 14100 | 0.0 | - |
| 0.8391 | 14150 | 0.0 | - |
| 0.8420 | 14200 | 0.0 | - |
| 0.8450 | 14250 | 0.0 | - |
| 0.8480 | 14300 | 0.0 | - |
| 0.8509 | 14350 | 0.0 | - |
| 0.8539 | 14400 | 0.0 | - |
| 0.8569 | 14450 | 0.0 | - |
| 0.8598 | 14500 | 0.0 | - |
| 0.8628 | 14550 | 0.0 | - |
| 0.8657 | 14600 | 0.0 | - |
| 0.8687 | 14650 | 0.0 | - |
| 0.8717 | 14700 | 0.0 | - |
| 0.8746 | 14750 | 0.0 | - |
| 0.8776 | 14800 | 0.0 | - |
| 0.8806 | 14850 | 0.0 | - |
| 0.8835 | 14900 | 0.0 | - |
| 0.8865 | 14950 | 0.0 | - |
| 0.8895 | 15000 | 0.0 | - |
| 0.8924 | 15050 | 0.0 | - |
| 0.8954 | 15100 | 0.0 | - |
| 0.8984 | 15150 | 0.0 | - |
| 0.9013 | 15200 | 0.0 | - |
| 0.9043 | 15250 | 0.0 | - |
| 0.9073 | 15300 | 0.0 | - |
| 0.9102 | 15350 | 0.0 | - |
| 0.9132 | 15400 | 0.0 | - |
| 0.9162 | 15450 | 0.0 | - |
| 0.9191 | 15500 | 0.0 | - |
| 0.9221 | 15550 | 0.0 | - |
| 0.9250 | 15600 | 0.0 | - |
| 0.9280 | 15650 | 0.0 | - |
| 0.9310 | 15700 | 0.0 | - |
| 0.9339 | 15750 | 0.0 | - |
| 0.9369 | 15800 | 0.0 | - |
| 0.9399 | 15850 | 0.0 | - |
| 0.9428 | 15900 | 0.0 | - |
| 0.9458 | 15950 | 0.0 | - |
| 0.9488 | 16000 | 0.0 | - |
| 0.9517 | 16050 | 0.0 | - |
| 0.9547 | 16100 | 0.0 | - |
| 0.9577 | 16150 | 0.0 | - |
| 0.9606 | 16200 | 0.0 | - |
| 0.9636 | 16250 | 0.0 | - |
| 0.9666 | 16300 | 0.0 | - |
| 0.9695 | 16350 | 0.0 | - |
| 0.9725 | 16400 | 0.0 | - |
| 0.9755 | 16450 | 0.0 | - |
| 0.9784 | 16500 | 0.0 | - |
| 0.9814 | 16550 | 0.0 | - |
| 0.9843 | 16600 | 0.0 | - |
| 0.9873 | 16650 | 0.0 | - |
| 0.9903 | 16700 | 0.0 | - |
| 0.9932 | 16750 | 0.0 | - |
| 0.9962 | 16800 | 0.0 | - |
| 0.9992 | 16850 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- 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}
}
```