---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: EPS:Why do I invest in $TSLA? Do I have blind faith? No. I closely watch their
EPS, their P/E, their products, their forecast. This is the only investment I
KNOW. And I know this is a great investment. I don’t say this to convince anyone.
These are my thoughts about my investment.
- text: EPS:$TSLA at 57x Street 2023 EPS (45x my 2023 EPS) seems an absurd valuation
for 50%+ volume/EPS growth fueled by the dual tailwinds of soaring EV adoption
and TSLA capacity. Investors seem overly worried Elon will sell more TSLA shares
even though he says “no further sales planned.” https://t.co/80siAfL847
- text: 'TSLA:Cars ... for delivery ? Most likely so. $TSLA #GigaBerlin https://t.co/XL6auHEYjZ'
- text: companies:Mainstream media has done an amazing job at brainwashing people.
Today at work, we were asked what companies we believe in & I said @Tesla
because they make the safest cars & EVERYONE disagreed with me because they
heard“they catch on fire & the batteries cost 20k to replace”
- text: 'cash flow:The market won’t be able to hold Tesla stock down longer, once
all factories are ramping and in full production.
There’s a certain point where the # of cars being produced, revenue & profit
& cash flow generated makes the valuation of Tesla look ridiculous.
$TSLA #Tesla'
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit Aspect Model 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: 0.9798115746971736
name: Accuracy
---
# SetFit Aspect Model with sentence-transformers/paraphrase-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/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. In particular, this model is in charge of filtering aspect span candidates.
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 this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## 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
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect](https://huggingface.co/NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-polarity](https://huggingface.co/NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-polarity)
- **Maximum Sequence Length:** 512 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 |
|:----------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| no aspect | - 'Tesla:Tesla could deliver 500K+ vehicles in Q4, increasing annual deliveries by 50%. Due to headwinds in 2022, now the manufacturer is ramping up production even harder to get as many EVs on the road as possible\n\n #Tesla $TSLA \nhttps://t.co/b2NCtZqDYn'
- 'vehicles:Tesla could deliver 500K+ vehicles in Q4, increasing annual deliveries by 50%. Due to headwinds in 2022, now the manufacturer is ramping up production even harder to get as many EVs on the road as possible\n\n #Tesla $TSLA \nhttps://t.co/b2NCtZqDYn'
- 'Q4:Tesla could deliver 500K+ vehicles in Q4, increasing annual deliveries by 50%. Due to headwinds in 2022, now the manufacturer is ramping up production even harder to get as many EVs on the road as possible\n\n #Tesla $TSLA \nhttps://t.co/b2NCtZqDYn'
|
| aspect | - "profit:I'm pretty sure, all an EV tax incentive will do, is raise the price of Teslas, at least for the next few years.\n\ni.e. just more profit for $TSLA\nAs if demand wasn't abundant enough already."
- "price:I'm pretty sure, all an EV tax incentive will do, is raise the price of Teslas, at least for the next few years.\n\ni.e. just more profit for $TSLA\nAs if demand wasn't abundant enough already."
- 'car:John Hennessey gets a $TSLA Plaid. \nA retired OEM executive describes Tesla as a $30k car with $70k in batteries. \nThe perfect description of a Tesla https://t.co/m5J5m3AuMJ'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9798 |
## 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(
"NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect",
"NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-polarity",
)
# 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 | 11 | 41.4789 | 57 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 560 |
| aspect | 33 |
### 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.2511 | - |
| 0.0025 | 50 | 0.2558 | - |
| 0.0051 | 100 | 0.2147 | - |
| 0.0076 | 150 | 0.2265 | - |
| 0.0101 | 200 | 0.2474 | - |
| 0.0127 | 250 | 0.2286 | - |
| 0.0152 | 300 | 0.1717 | - |
| 0.0178 | 350 | 0.0737 | - |
| 0.0203 | 400 | 0.0231 | - |
| 0.0228 | 450 | 0.0069 | - |
| 0.0254 | 500 | 0.0032 | - |
| 0.0279 | 550 | 0.002 | - |
| 0.0304 | 600 | 0.0008 | - |
| 0.0330 | 650 | 0.0023 | - |
| 0.0355 | 700 | 0.002 | - |
| 0.0381 | 750 | 0.0008 | - |
| 0.0406 | 800 | 0.0019 | - |
| 0.0431 | 850 | 0.0003 | - |
| 0.0457 | 900 | 0.0004 | - |
| 0.0482 | 950 | 0.0005 | - |
| 0.0507 | 1000 | 0.0003 | - |
| 0.0533 | 1050 | 0.0006 | - |
| 0.0558 | 1100 | 0.0071 | - |
| 0.0584 | 1150 | 0.0001 | - |
| 0.0609 | 1200 | 0.0001 | - |
| 0.0634 | 1250 | 0.0001 | - |
| 0.0660 | 1300 | 0.0001 | - |
| 0.0685 | 1350 | 0.0004 | - |
| 0.0710 | 1400 | 0.0001 | - |
| 0.0736 | 1450 | 0.0002 | - |
| 0.0761 | 1500 | 0.0002 | - |
| 0.0787 | 1550 | 0.0002 | - |
| 0.0812 | 1600 | 0.0001 | - |
| 0.0837 | 1650 | 0.0001 | - |
| 0.0863 | 1700 | 0.0007 | - |
| 0.0888 | 1750 | 0.0001 | - |
| 0.0913 | 1800 | 0.0002 | - |
| 0.0939 | 1850 | 0.0011 | - |
| 0.0964 | 1900 | 0.0007 | - |
| 0.0990 | 1950 | 0.001 | - |
| 0.1015 | 2000 | 0.0003 | - |
| 0.1040 | 2050 | 0.0004 | - |
| 0.1066 | 2100 | 0.0006 | - |
| 0.1091 | 2150 | 0.0004 | - |
| 0.1116 | 2200 | 0.0 | - |
| 0.1142 | 2250 | 0.0 | - |
| 0.1167 | 2300 | 0.0001 | - |
| 0.1193 | 2350 | 0.0017 | - |
| 0.1218 | 2400 | 0.0007 | - |
| 0.1243 | 2450 | 0.0023 | - |
| 0.1269 | 2500 | 0.0 | - |
| 0.1294 | 2550 | 0.0 | - |
| 0.1319 | 2600 | 0.0007 | - |
| 0.1345 | 2650 | 0.0 | - |
| 0.1370 | 2700 | 0.0004 | - |
| 0.1396 | 2750 | 0.0001 | - |
| 0.1421 | 2800 | 0.0002 | - |
| 0.1446 | 2850 | 0.0019 | - |
| 0.1472 | 2900 | 0.0002 | - |
| 0.1497 | 2950 | 0.0001 | - |
| 0.1522 | 3000 | 0.0 | - |
| 0.1548 | 3050 | 0.0001 | - |
| 0.1573 | 3100 | 0.0 | - |
| 0.1598 | 3150 | 0.0001 | - |
| 0.1624 | 3200 | 0.0007 | - |
| 0.1649 | 3250 | 0.0 | - |
| 0.1675 | 3300 | 0.0002 | - |
| 0.1700 | 3350 | 0.0004 | - |
| 0.1725 | 3400 | 0.0 | - |
| 0.1751 | 3450 | 0.0 | - |
| 0.1776 | 3500 | 0.0 | - |
| 0.1801 | 3550 | 0.0 | - |
| 0.1827 | 3600 | 0.0001 | - |
| 0.1852 | 3650 | 0.0 | - |
| 0.1878 | 3700 | 0.0001 | - |
| 0.1903 | 3750 | 0.0 | - |
| 0.1928 | 3800 | 0.0 | - |
| 0.1954 | 3850 | 0.0 | - |
| 0.1979 | 3900 | 0.0 | - |
| 0.2004 | 3950 | 0.0 | - |
| 0.2030 | 4000 | 0.0 | - |
| 0.2055 | 4050 | 0.0019 | - |
| 0.2081 | 4100 | 0.0 | - |
| 0.2106 | 4150 | 0.0001 | - |
| 0.2131 | 4200 | 0.0 | - |
| 0.2157 | 4250 | 0.0 | - |
| 0.2182 | 4300 | 0.0 | - |
| 0.2207 | 4350 | 0.0 | - |
| 0.2233 | 4400 | 0.0005 | - |
| 0.2258 | 4450 | 0.0 | - |
| 0.2284 | 4500 | 0.0 | - |
| 0.2309 | 4550 | 0.0 | - |
| 0.2334 | 4600 | 0.0 | - |
| 0.2360 | 4650 | 0.0 | - |
| 0.2385 | 4700 | 0.0009 | - |
| 0.2410 | 4750 | 0.0 | - |
| 0.2436 | 4800 | 0.0 | - |
| 0.2461 | 4850 | 0.0 | - |
| 0.2487 | 4900 | 0.0002 | - |
| 0.2512 | 4950 | 0.0 | - |
| 0.2537 | 5000 | 0.0011 | - |
| 0.2563 | 5050 | 0.0 | - |
| 0.2588 | 5100 | 0.0 | - |
| 0.2613 | 5150 | 0.0 | - |
| 0.2639 | 5200 | 0.0 | - |
| 0.2664 | 5250 | 0.0 | - |
| 0.2690 | 5300 | 0.0 | - |
| 0.2715 | 5350 | 0.0026 | - |
| 0.2740 | 5400 | 0.0 | - |
| 0.2766 | 5450 | 0.0021 | - |
| 0.2791 | 5500 | 0.0 | - |
| 0.2816 | 5550 | 0.0001 | - |
| 0.2842 | 5600 | 0.0 | - |
| 0.2867 | 5650 | 0.0001 | - |
| 0.2893 | 5700 | 0.0 | - |
| 0.2918 | 5750 | 0.0 | - |
| 0.2943 | 5800 | 0.0 | - |
| 0.2969 | 5850 | 0.0 | - |
| 0.2994 | 5900 | 0.0 | - |
| 0.3019 | 5950 | 0.0 | - |
| 0.3045 | 6000 | 0.0 | - |
| 0.3070 | 6050 | 0.0 | - |
| 0.3096 | 6100 | 0.0 | - |
| 0.3121 | 6150 | 0.0003 | - |
| 0.3146 | 6200 | 0.0 | - |
| 0.3172 | 6250 | 0.0 | - |
| 0.3197 | 6300 | 0.0 | - |
| 0.3222 | 6350 | 0.0001 | - |
| 0.3248 | 6400 | 0.0009 | - |
| 0.3273 | 6450 | 0.0 | - |
| 0.3298 | 6500 | 0.0 | - |
| 0.3324 | 6550 | 0.0 | - |
| 0.3349 | 6600 | 0.0 | - |
| 0.3375 | 6650 | 0.0 | - |
| 0.3400 | 6700 | 0.0 | - |
| 0.3425 | 6750 | 0.0 | - |
| 0.3451 | 6800 | 0.0 | - |
| 0.3476 | 6850 | 0.0 | - |
| 0.3501 | 6900 | 0.0 | - |
| 0.3527 | 6950 | 0.0 | - |
| 0.3552 | 7000 | 0.0 | - |
| 0.3578 | 7050 | 0.0 | - |
| 0.3603 | 7100 | 0.0536 | - |
| 0.3628 | 7150 | 0.0 | - |
| 0.3654 | 7200 | 0.0 | - |
| 0.3679 | 7250 | 0.0 | - |
| 0.3704 | 7300 | 0.0 | - |
| 0.3730 | 7350 | 0.0 | - |
| 0.3755 | 7400 | 0.0 | - |
| 0.3781 | 7450 | 0.0 | - |
| 0.3806 | 7500 | 0.0 | - |
| 0.3831 | 7550 | 0.0 | - |
| 0.3857 | 7600 | 0.0 | - |
| 0.3882 | 7650 | 0.0 | - |
| 0.3907 | 7700 | 0.0 | - |
| 0.3933 | 7750 | 0.0019 | - |
| 0.3958 | 7800 | 0.0 | - |
| 0.3984 | 7850 | 0.0 | - |
| 0.4009 | 7900 | 0.0548 | - |
| 0.4034 | 7950 | 0.0 | - |
| 0.4060 | 8000 | 0.0053 | - |
| 0.4085 | 8050 | 0.0 | - |
| 0.4110 | 8100 | 0.0 | - |
| 0.4136 | 8150 | 0.0 | - |
| 0.4161 | 8200 | 0.0 | - |
| 0.4187 | 8250 | 0.0624 | - |
| 0.4212 | 8300 | 0.0622 | - |
| 0.4237 | 8350 | 0.0618 | - |
| 0.4263 | 8400 | 0.0001 | - |
| 0.4288 | 8450 | 0.0 | - |
| 0.4313 | 8500 | 0.0001 | - |
| 0.4339 | 8550 | 0.0 | - |
| 0.4364 | 8600 | 0.0 | - |
| 0.4390 | 8650 | 0.0 | - |
| 0.4415 | 8700 | 0.0012 | - |
| 0.4440 | 8750 | 0.0001 | - |
| 0.4466 | 8800 | 0.0005 | - |
| 0.4491 | 8850 | 0.0 | - |
| 0.4516 | 8900 | 0.0 | - |
| 0.4542 | 8950 | 0.0 | - |
| 0.4567 | 9000 | 0.0 | - |
| 0.4593 | 9050 | 0.0 | - |
| 0.4618 | 9100 | 0.0 | - |
| 0.4643 | 9150 | 0.0 | - |
| 0.4669 | 9200 | 0.0 | - |
| 0.4694 | 9250 | 0.0408 | - |
| 0.4719 | 9300 | 0.0498 | - |
| 0.4745 | 9350 | 0.0 | - |
| 0.4770 | 9400 | 0.0 | - |
| 0.4795 | 9450 | 0.0017 | - |
| 0.4821 | 9500 | 0.0 | - |
| 0.4846 | 9550 | 0.0 | - |
| 0.4872 | 9600 | 0.0 | - |
| 0.4897 | 9650 | 0.0 | - |
| 0.4922 | 9700 | 0.0 | - |
| 0.4948 | 9750 | 0.0 | - |
| 0.4973 | 9800 | 0.0589 | - |
| 0.4998 | 9850 | 0.0 | - |
| 0.5024 | 9900 | 0.0 | - |
| 0.5049 | 9950 | 0.0015 | - |
| 0.5075 | 10000 | 0.0 | - |
| 0.5100 | 10050 | 0.0 | - |
| 0.5125 | 10100 | 0.0 | - |
| 0.5151 | 10150 | 0.0 | - |
| 0.5176 | 10200 | 0.0 | - |
| 0.5201 | 10250 | 0.0 | - |
| 0.5227 | 10300 | 0.0013 | - |
| 0.5252 | 10350 | 0.0023 | - |
| 0.5278 | 10400 | 0.0 | - |
| 0.5303 | 10450 | 0.0 | - |
| 0.5328 | 10500 | 0.0 | - |
| 0.5354 | 10550 | 0.0003 | - |
| 0.5379 | 10600 | 0.0 | - |
| 0.5404 | 10650 | 0.0 | - |
| 0.5430 | 10700 | 0.0002 | - |
| 0.5455 | 10750 | 0.0 | - |
| 0.5481 | 10800 | 0.0 | - |
| 0.5506 | 10850 | 0.0005 | - |
| 0.5531 | 10900 | 0.0 | - |
| 0.5557 | 10950 | 0.0 | - |
| 0.5582 | 11000 | 0.0 | - |
| 0.5607 | 11050 | 0.0 | - |
| 0.5633 | 11100 | 0.0 | - |
| 0.5658 | 11150 | 0.0 | - |
| 0.5684 | 11200 | 0.0 | - |
| 0.5709 | 11250 | 0.0 | - |
| 0.5734 | 11300 | 0.0 | - |
| 0.5760 | 11350 | 0.0008 | - |
| 0.5785 | 11400 | 0.0 | - |
| 0.5810 | 11450 | 0.0024 | - |
| 0.5836 | 11500 | 0.0 | - |
| 0.5861 | 11550 | 0.0 | - |
| 0.5887 | 11600 | 0.0 | - |
| 0.5912 | 11650 | 0.0 | - |
| 0.5937 | 11700 | 0.001 | - |
| 0.5963 | 11750 | 0.0 | - |
| 0.5988 | 11800 | 0.0 | - |
| 0.6013 | 11850 | 0.0 | - |
| 0.6039 | 11900 | 0.0527 | - |
| 0.6064 | 11950 | 0.0021 | - |
| 0.6090 | 12000 | 0.0 | - |
| 0.6115 | 12050 | 0.0 | - |
| 0.6140 | 12100 | 0.0 | - |
| 0.6166 | 12150 | 0.0 | - |
| 0.6191 | 12200 | 0.0 | - |
| 0.6216 | 12250 | 0.0 | - |
| 0.6242 | 12300 | 0.0 | - |
| 0.6267 | 12350 | 0.0006 | - |
| 0.6292 | 12400 | 0.0 | - |
| 0.6318 | 12450 | 0.0 | - |
| 0.6343 | 12500 | 0.001 | - |
| 0.6369 | 12550 | 0.0017 | - |
| 0.6394 | 12600 | 0.0 | - |
| 0.6419 | 12650 | 0.0 | - |
| 0.6445 | 12700 | 0.0 | - |
| 0.6470 | 12750 | 0.0012 | - |
| 0.6495 | 12800 | 0.0 | - |
| 0.6521 | 12850 | 0.0 | - |
| 0.6546 | 12900 | 0.0 | - |
| 0.6572 | 12950 | 0.0434 | - |
| 0.6597 | 13000 | 0.0 | - |
| 0.6622 | 13050 | 0.0 | - |
| 0.6648 | 13100 | 0.0003 | - |
| 0.6673 | 13150 | 0.0 | - |
| 0.6698 | 13200 | 0.0 | - |
| 0.6724 | 13250 | 0.0003 | - |
| 0.6749 | 13300 | 0.0 | - |
| 0.6775 | 13350 | 0.0 | - |
| 0.6800 | 13400 | 0.0005 | - |
| 0.6825 | 13450 | 0.0 | - |
| 0.6851 | 13500 | 0.0011 | - |
| 0.6876 | 13550 | 0.0475 | - |
| 0.6901 | 13600 | 0.0 | - |
| 0.6927 | 13650 | 0.0007 | - |
| 0.6952 | 13700 | 0.0 | - |
| 0.6978 | 13750 | 0.0 | - |
| 0.7003 | 13800 | 0.0 | - |
| 0.7028 | 13850 | 0.0 | - |
| 0.7054 | 13900 | 0.0 | - |
| 0.7079 | 13950 | 0.0015 | - |
| 0.7104 | 14000 | 0.0034 | - |
| 0.7130 | 14050 | 0.0009 | - |
| 0.7155 | 14100 | 0.0 | - |
| 0.7181 | 14150 | 0.0009 | - |
| 0.7206 | 14200 | 0.0 | - |
| 0.7231 | 14250 | 0.0003 | - |
| 0.7257 | 14300 | 0.0004 | - |
| 0.7282 | 14350 | 0.0 | - |
| 0.7307 | 14400 | 0.0003 | - |
| 0.7333 | 14450 | 0.0 | - |
| 0.7358 | 14500 | 0.0 | - |
| 0.7384 | 14550 | 0.0 | - |
| 0.7409 | 14600 | 0.0 | - |
| 0.7434 | 14650 | 0.0 | - |
| 0.7460 | 14700 | 0.0018 | - |
| 0.7485 | 14750 | 0.0012 | - |
| 0.7510 | 14800 | 0.0 | - |
| 0.7536 | 14850 | 0.0 | - |
| 0.7561 | 14900 | 0.0013 | - |
| 0.7587 | 14950 | 0.0 | - |
| 0.7612 | 15000 | 0.0 | - |
| 0.7637 | 15050 | 0.0 | - |
| 0.7663 | 15100 | 0.0 | - |
| 0.7688 | 15150 | 0.0 | - |
| 0.7713 | 15200 | 0.0 | - |
| 0.7739 | 15250 | 0.0 | - |
| 0.7764 | 15300 | 0.0 | - |
| 0.7790 | 15350 | 0.0 | - |
| 0.7815 | 15400 | 0.0 | - |
| 0.7840 | 15450 | 0.0 | - |
| 0.7866 | 15500 | 0.0 | - |
| 0.7891 | 15550 | 0.0 | - |
| 0.7916 | 15600 | 0.0004 | - |
| 0.7942 | 15650 | 0.0005 | - |
| 0.7967 | 15700 | 0.0 | - |
| 0.7992 | 15750 | 0.0 | - |
| 0.8018 | 15800 | 0.0 | - |
| 0.8043 | 15850 | 0.0 | - |
| 0.8069 | 15900 | 0.0 | - |
| 0.8094 | 15950 | 0.0555 | - |
| 0.8119 | 16000 | 0.0 | - |
| 0.8145 | 16050 | 0.0 | - |
| 0.8170 | 16100 | 0.0 | - |
| 0.8195 | 16150 | 0.0 | - |
| 0.8221 | 16200 | 0.0 | - |
| 0.8246 | 16250 | 0.0007 | - |
| 0.8272 | 16300 | 0.0 | - |
| 0.8297 | 16350 | 0.0 | - |
| 0.8322 | 16400 | 0.0 | - |
| 0.8348 | 16450 | 0.0003 | - |
| 0.8373 | 16500 | 0.0 | - |
| 0.8398 | 16550 | 0.0012 | - |
| 0.8424 | 16600 | 0.0 | - |
| 0.8449 | 16650 | 0.0 | - |
| 0.8475 | 16700 | 0.0 | - |
| 0.8500 | 16750 | 0.0 | - |
| 0.8525 | 16800 | 0.0 | - |
| 0.8551 | 16850 | 0.0 | - |
| 0.8576 | 16900 | 0.0007 | - |
| 0.8601 | 16950 | 0.0 | - |
| 0.8627 | 17000 | 0.001 | - |
| 0.8652 | 17050 | 0.0 | - |
| 0.8678 | 17100 | 0.0 | - |
| 0.8703 | 17150 | 0.0 | - |
| 0.8728 | 17200 | 0.0 | - |
| 0.8754 | 17250 | 0.0 | - |
| 0.8779 | 17300 | 0.0 | - |
| 0.8804 | 17350 | 0.0 | - |
| 0.8830 | 17400 | 0.0007 | - |
| 0.8855 | 17450 | 0.0 | - |
| 0.8881 | 17500 | 0.0 | - |
| 0.8906 | 17550 | 0.0505 | - |
| 0.8931 | 17600 | 0.0 | - |
| 0.8957 | 17650 | 0.0 | - |
| 0.8982 | 17700 | 0.0008 | - |
| 0.9007 | 17750 | 0.0 | - |
| 0.9033 | 17800 | 0.0003 | - |
| 0.9058 | 17850 | 0.0 | - |
| 0.9084 | 17900 | 0.0 | - |
| 0.9109 | 17950 | 0.0009 | - |
| 0.9134 | 18000 | 0.0 | - |
| 0.9160 | 18050 | 0.0 | - |
| 0.9185 | 18100 | 0.0 | - |
| 0.9210 | 18150 | 0.0 | - |
| 0.9236 | 18200 | 0.0 | - |
| 0.9261 | 18250 | 0.0 | - |
| 0.9287 | 18300 | 0.0 | - |
| 0.9312 | 18350 | 0.0008 | - |
| 0.9337 | 18400 | 0.0 | - |
| 0.9363 | 18450 | 0.0 | - |
| 0.9388 | 18500 | 0.0 | - |
| 0.9413 | 18550 | 0.0 | - |
| 0.9439 | 18600 | 0.0 | - |
| 0.9464 | 18650 | 0.0 | - |
| 0.9489 | 18700 | 0.0 | - |
| 0.9515 | 18750 | 0.0 | - |
| 0.9540 | 18800 | 0.0 | - |
| 0.9566 | 18850 | 0.0 | - |
| 0.9591 | 18900 | 0.0 | - |
| 0.9616 | 18950 | 0.0 | - |
| 0.9642 | 19000 | 0.0 | - |
| 0.9667 | 19050 | 0.0 | - |
| 0.9692 | 19100 | 0.0 | - |
| 0.9718 | 19150 | 0.0 | - |
| 0.9743 | 19200 | 0.0 | - |
| 0.9769 | 19250 | 0.0 | - |
| 0.9794 | 19300 | 0.0005 | - |
| 0.9819 | 19350 | 0.0 | - |
| 0.9845 | 19400 | 0.0 | - |
| 0.9870 | 19450 | 0.0 | - |
| 0.9895 | 19500 | 0.0 | - |
| 0.9921 | 19550 | 0.0011 | - |
| 0.9946 | 19600 | 0.0 | - |
| 0.9972 | 19650 | 0.0 | - |
| 0.9997 | 19700 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- spaCy: 3.6.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1
## 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}
}
```