---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: 'brand''s product, powered by product, is making waves by potentially surpassing
brand''s product in ai performance. lets not forget massive developments in ai
from brand, brand, brand and 5 new tools here''s what you need to know:'
- text: 'well... brand launches product tomorrow so it''s going to be much more exciting
than 2x! product ca: 0x09e5e172df245529b22686b77e959d3f2937feb0'
- text: 'brand''s product is product''s newest and greatest competitor yet: here''s
how you can use it within product dlvr.it/szs9nh'
- text: bad actors exploit product to write malicious codes product, ever since its
launch in november last year, has been making lots of noise. with creators experimenting
with it and getting varied results, the product became an acceptable product tool
that couldlnkd.in/drbvpbdt
- text: testing out product. i find it incredibly useful. one way to monetize it is
simply to put paid links related to the search
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-base-en-v1.5
model-index:
- name: SetFit with BAAI/bge-base-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.86
name: Accuracy
- type: f1
value:
- 0.2857142857142857
- 0.5945945945945945
- 0.9195402298850575
name: F1
- type: precision
value:
- 1.0
- 0.9166666666666666
- 0.8547008547008547
name: Precision
- type: recall
value:
- 0.16666666666666666
- 0.44
- 0.9950248756218906
name: Recall
---
# SetFit with BAAI/bge-base-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
- **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 |
|:--------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neither | - 'ai becomes so much easier to spot when you realize it can replicate, but never understand. its why product usually gives its answers in lists. its a standardized format meant to hide its ignorance to prose.'
- "hakeem jeffries' tweets are getting so productian it's not even funny and boring any more. he may have brand cranking these out."
- 'have you tried this with product? i did this with music and got amazing results'
|
| peak | - 'thats rad man. i have adhd and dyslexia and some other cognitive disabilities and honestly brand is a lifesaver.'
- "product is like having a coding partner that understands my style, enhancing my productivity significantly. i've even changed the way i code. my code and process is more modular so it's easier to use the output from product in my code base!"
- 'product is an incredible tool for explaining concepts in i prompted it to describe how k-means clustering could be applied to an engagement survey. it generated sample data, explained the concept and how the insights could be applied.'
|
| pit | - 'many similar posts popping up on my timeline frustrated with chatproduct not performing to previous levels defeats the purpose of having an ai assitant available 24/7 if it never wants to do any of the tasks you ask of it'
- "the stuff brand gives is entirely too scripted *and* impractical, which is what i'm trying to avoid:/"
- 'so disappointed theyve programmed product to think starvation mode is real'
|
## Evaluation
### Metrics
| Label | Accuracy | F1 | Precision | Recall |
|:--------|:---------|:-------------------------------------------------------------|:----------------------------------------------|:------------------------------------------------|
| **all** | 0.86 | [0.2857142857142857, 0.5945945945945945, 0.9195402298850575] | [1.0, 0.9166666666666666, 0.8547008547008547] | [0.16666666666666666, 0.44, 0.9950248756218906] |
## 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("jamiehudson/725_model_v3")
# Run inference
preds = model("brand's product is product's newest and greatest competitor yet: here's how you can use it within product dlvr.it/szs9nh")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 27.8534 | 91 |
| Label | Training Sample Count |
|:--------|:----------------------|
| pit | 26 |
| peak | 51 |
| neither | 1137 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0012 | 1 | 0.2612 | - |
| 0.0621 | 50 | 0.2009 | - |
| 0.1242 | 100 | 0.0339 | - |
| 0.1863 | 150 | 0.0062 | - |
| 0.2484 | 200 | 0.0039 | - |
| 0.3106 | 250 | 0.0017 | - |
| 0.3727 | 300 | 0.003 | - |
| 0.4348 | 350 | 0.0015 | - |
| 0.4969 | 400 | 0.002 | - |
| 0.5590 | 450 | 0.0022 | - |
| 0.6211 | 500 | 0.0013 | - |
| 0.6832 | 550 | 0.0013 | - |
| 0.7453 | 600 | 0.0014 | - |
| 0.8075 | 650 | 0.0014 | - |
| 0.8696 | 700 | 0.0012 | - |
| 0.9317 | 750 | 0.0014 | - |
| 0.9938 | 800 | 0.0016 | - |
| 0.0000 | 1 | 0.0897 | - |
| 0.0012 | 50 | 0.1107 | - |
| 0.0025 | 100 | 0.065 | - |
| 0.0037 | 150 | 0.1892 | - |
| 0.0049 | 200 | 0.0774 | - |
| 0.0062 | 250 | 0.0391 | - |
| 0.0074 | 300 | 0.117 | - |
| 0.0086 | 350 | 0.0954 | - |
| 0.0099 | 400 | 0.0292 | - |
| 0.0111 | 450 | 0.0327 | - |
| 0.0123 | 500 | 0.0041 | - |
| 0.0136 | 550 | 0.0018 | - |
| 0.0148 | 600 | 0.03 | - |
| 0.0160 | 650 | 0.0015 | - |
| 0.0173 | 700 | 0.0036 | - |
| 0.0185 | 750 | 0.0182 | - |
| 0.0197 | 800 | 0.0017 | - |
| 0.0210 | 850 | 0.0012 | - |
| 0.0222 | 900 | 0.0014 | - |
| 0.0234 | 950 | 0.0011 | - |
| 0.0247 | 1000 | 0.0014 | - |
| 0.0259 | 1050 | 0.0301 | - |
| 0.0271 | 1100 | 0.001 | - |
| 0.0284 | 1150 | 0.0011 | - |
| 0.0296 | 1200 | 0.0009 | - |
| 0.0308 | 1250 | 0.0011 | - |
| 0.0321 | 1300 | 0.0012 | - |
| 0.0333 | 1350 | 0.001 | - |
| 0.0345 | 1400 | 0.0008 | - |
| 0.0358 | 1450 | 0.005 | - |
| 0.0370 | 1500 | 0.0008 | - |
| 0.0382 | 1550 | 0.0044 | - |
| 0.0395 | 1600 | 0.0008 | - |
| 0.0407 | 1650 | 0.0007 | - |
| 0.0419 | 1700 | 0.0014 | - |
| 0.0432 | 1750 | 0.0006 | - |
| 0.0444 | 1800 | 0.001 | - |
| 0.0456 | 1850 | 0.0007 | - |
| 0.0469 | 1900 | 0.0006 | - |
| 0.0481 | 1950 | 0.0006 | - |
| 0.0493 | 2000 | 0.0005 | - |
| 0.0506 | 2050 | 0.0006 | - |
| 0.0518 | 2100 | 0.0041 | - |
| 0.0530 | 2150 | 0.0006 | - |
| 0.0543 | 2200 | 0.0006 | - |
| 0.0555 | 2250 | 0.0007 | - |
| 0.0567 | 2300 | 0.0006 | - |
| 0.0580 | 2350 | 0.0005 | - |
| 0.0592 | 2400 | 0.0007 | - |
| 0.0604 | 2450 | 0.0005 | - |
| 0.0617 | 2500 | 0.0004 | - |
| 0.0629 | 2550 | 0.0005 | - |
| 0.0641 | 2600 | 0.0004 | - |
| 0.0654 | 2650 | 0.0007 | - |
| 0.0666 | 2700 | 0.0004 | - |
| 0.0678 | 2750 | 0.0005 | - |
| 0.0691 | 2800 | 0.0004 | - |
| 0.0703 | 2850 | 0.0004 | - |
| 0.0715 | 2900 | 0.0004 | - |
| 0.0728 | 2950 | 0.0005 | - |
| 0.0740 | 3000 | 0.0004 | - |
| 0.0752 | 3050 | 0.0004 | - |
| 0.0765 | 3100 | 0.0003 | - |
| 0.0777 | 3150 | 0.0003 | - |
| 0.0789 | 3200 | 0.0003 | - |
| 0.0802 | 3250 | 0.0003 | - |
| 0.0814 | 3300 | 0.0004 | - |
| 0.0826 | 3350 | 0.0003 | - |
| 0.0839 | 3400 | 0.0003 | - |
| 0.0851 | 3450 | 0.0007 | - |
| 0.0863 | 3500 | 0.0003 | - |
| 0.0876 | 3550 | 0.0003 | - |
| 0.0888 | 3600 | 0.0004 | - |
| 0.0900 | 3650 | 0.0003 | - |
| 0.0913 | 3700 | 0.0003 | - |
| 0.0925 | 3750 | 0.0004 | - |
| 0.0937 | 3800 | 0.0004 | - |
| 0.0950 | 3850 | 0.0232 | - |
| 0.0962 | 3900 | 0.0004 | - |
| 0.0974 | 3950 | 0.0165 | - |
| 0.0987 | 4000 | 0.0003 | - |
| 0.0999 | 4050 | 0.0229 | - |
| 0.1011 | 4100 | 0.0004 | - |
| 0.1024 | 4150 | 0.0003 | - |
| 0.1036 | 4200 | 0.0004 | - |
| 0.1048 | 4250 | 0.0002 | - |
| 0.1061 | 4300 | 0.0002 | - |
| 0.1073 | 4350 | 0.0002 | - |
| 0.1085 | 4400 | 0.0003 | - |
| 0.1098 | 4450 | 0.0002 | - |
| 0.1110 | 4500 | 0.0002 | - |
| 0.1122 | 4550 | 0.0003 | - |
| 0.1135 | 4600 | 0.0002 | - |
| 0.1147 | 4650 | 0.0002 | - |
| 0.1159 | 4700 | 0.0002 | - |
| 0.1172 | 4750 | 0.0002 | - |
| 0.1184 | 4800 | 0.0002 | - |
| 0.1196 | 4850 | 0.0002 | - |
| 0.1209 | 4900 | 0.0002 | - |
| 0.1221 | 4950 | 0.0002 | - |
| 0.1233 | 5000 | 0.0002 | - |
| 0.1246 | 5050 | 0.0002 | - |
| 0.1258 | 5100 | 0.0002 | - |
| 0.1270 | 5150 | 0.0003 | - |
| 0.1283 | 5200 | 0.0001 | - |
| 0.1295 | 5250 | 0.0002 | - |
| 0.1307 | 5300 | 0.0002 | - |
| 0.1320 | 5350 | 0.0002 | - |
| 0.1332 | 5400 | 0.0001 | - |
| 0.1344 | 5450 | 0.0002 | - |
| 0.1357 | 5500 | 0.0002 | - |
| 0.1369 | 5550 | 0.0002 | - |
| 0.1381 | 5600 | 0.0001 | - |
| 0.1394 | 5650 | 0.0001 | - |
| 0.1406 | 5700 | 0.0001 | - |
| 0.1418 | 5750 | 0.0001 | - |
| 0.1431 | 5800 | 0.0001 | - |
| 0.1443 | 5850 | 0.0001 | - |
| 0.1455 | 5900 | 0.0001 | - |
| 0.1468 | 5950 | 0.0002 | - |
| 0.1480 | 6000 | 0.0001 | - |
| 0.1492 | 6050 | 0.0002 | - |
| 0.1505 | 6100 | 0.0002 | - |
| 0.1517 | 6150 | 0.0004 | - |
| 0.1529 | 6200 | 0.0003 | - |
| 0.1542 | 6250 | 0.0001 | - |
| 0.1554 | 6300 | 0.0003 | - |
| 0.1566 | 6350 | 0.0001 | - |
| 0.1579 | 6400 | 0.0001 | - |
| 0.1591 | 6450 | 0.0002 | - |
| 0.1603 | 6500 | 0.0001 | - |
| 0.1616 | 6550 | 0.0001 | - |
| 0.1628 | 6600 | 0.0001 | - |
| 0.1640 | 6650 | 0.0001 | - |
| 0.1653 | 6700 | 0.0002 | - |
| 0.1665 | 6750 | 0.0001 | - |
| 0.1677 | 6800 | 0.0001 | - |
| 0.1690 | 6850 | 0.0001 | - |
| 0.1702 | 6900 | 0.0001 | - |
| 0.1714 | 6950 | 0.0001 | - |
| 0.1727 | 7000 | 0.0001 | - |
| 0.1739 | 7050 | 0.0001 | - |
| 0.1751 | 7100 | 0.0001 | - |
| 0.1764 | 7150 | 0.0001 | - |
| 0.1776 | 7200 | 0.0001 | - |
| 0.1788 | 7250 | 0.0001 | - |
| 0.1801 | 7300 | 0.0001 | - |
| 0.1813 | 7350 | 0.0001 | - |
| 0.1825 | 7400 | 0.0001 | - |
| 0.1838 | 7450 | 0.0001 | - |
| 0.1850 | 7500 | 0.0001 | - |
| 0.1862 | 7550 | 0.0001 | - |
| 0.1875 | 7600 | 0.0 | - |
| 0.1887 | 7650 | 0.0001 | - |
| 0.1899 | 7700 | 0.0001 | - |
| 0.1912 | 7750 | 0.0001 | - |
| 0.1924 | 7800 | 0.0001 | - |
| 0.1936 | 7850 | 0.0 | - |
| 0.1949 | 7900 | 0.0001 | - |
| 0.1961 | 7950 | 0.0 | - |
| 0.1973 | 8000 | 0.0001 | - |
| 0.1986 | 8050 | 0.0 | - |
| 0.1998 | 8100 | 0.0 | - |
| 0.2010 | 8150 | 0.0 | - |
| 0.2023 | 8200 | 0.0 | - |
| 0.2035 | 8250 | 0.0 | - |
| 0.2047 | 8300 | 0.0 | - |
| 0.2060 | 8350 | 0.0 | - |
| 0.2072 | 8400 | 0.0001 | - |
| 0.2084 | 8450 | 0.0 | - |
| 0.2097 | 8500 | 0.0002 | - |
| 0.2109 | 8550 | 0.0 | - |
| 0.2121 | 8600 | 0.0 | - |
| 0.2134 | 8650 | 0.0 | - |
| 0.2146 | 8700 | 0.0 | - |
| 0.2158 | 8750 | 0.0001 | - |
| 0.2171 | 8800 | 0.0002 | - |
| 0.2183 | 8850 | 0.0 | - |
| 0.2195 | 8900 | 0.0001 | - |
| 0.2208 | 8950 | 0.0 | - |
| 0.2220 | 9000 | 0.0 | - |
| 0.2232 | 9050 | 0.0 | - |
| 0.2245 | 9100 | 0.0 | - |
| 0.2257 | 9150 | 0.0 | - |
| 0.2269 | 9200 | 0.0 | - |
| 0.2282 | 9250 | 0.0 | - |
| 0.2294 | 9300 | 0.0 | - |
| 0.2306 | 9350 | 0.0 | - |
| 0.2319 | 9400 | 0.0 | - |
| 0.2331 | 9450 | 0.0 | - |
| 0.2343 | 9500 | 0.0 | - |
| 0.2356 | 9550 | 0.0 | - |
| 0.2368 | 9600 | 0.0 | - |
| 0.2380 | 9650 | 0.0 | - |
| 0.2393 | 9700 | 0.0 | - |
| 0.2405 | 9750 | 0.0 | - |
| 0.2417 | 9800 | 0.0 | - |
| 0.2430 | 9850 | 0.0 | - |
| 0.2442 | 9900 | 0.0 | - |
| 0.2454 | 9950 | 0.0 | - |
| 0.2467 | 10000 | 0.0 | - |
| 0.2479 | 10050 | 0.0 | - |
| 0.2491 | 10100 | 0.0 | - |
| 0.2504 | 10150 | 0.0 | - |
| 0.2516 | 10200 | 0.0 | - |
| 0.2528 | 10250 | 0.0 | - |
| 0.2541 | 10300 | 0.0001 | - |
| 0.2553 | 10350 | 0.0001 | - |
| 0.2565 | 10400 | 0.0 | - |
| 0.2578 | 10450 | 0.0 | - |
| 0.2590 | 10500 | 0.0 | - |
| 0.2602 | 10550 | 0.0 | - |
| 0.2615 | 10600 | 0.0 | - |
| 0.2627 | 10650 | 0.0 | - |
| 0.2639 | 10700 | 0.0 | - |
| 0.2652 | 10750 | 0.0 | - |
| 0.2664 | 10800 | 0.0 | - |
| 0.2676 | 10850 | 0.0 | - |
| 0.2689 | 10900 | 0.0 | - |
| 0.2701 | 10950 | 0.0 | - |
| 0.2713 | 11000 | 0.0 | - |
| 0.2726 | 11050 | 0.0 | - |
| 0.2738 | 11100 | 0.0 | - |
| 0.2750 | 11150 | 0.0 | - |
| 0.2763 | 11200 | 0.0 | - |
| 0.2775 | 11250 | 0.0 | - |
| 0.2787 | 11300 | 0.0 | - |
| 0.2800 | 11350 | 0.0 | - |
| 0.2812 | 11400 | 0.0 | - |
| 0.2824 | 11450 | 0.0 | - |
| 0.2837 | 11500 | 0.0 | - |
| 0.2849 | 11550 | 0.0 | - |
| 0.2861 | 11600 | 0.0 | - |
| 0.2874 | 11650 | 0.0001 | - |
| 0.2886 | 11700 | 0.0301 | - |
| 0.2898 | 11750 | 0.0 | - |
| 0.2911 | 11800 | 0.0 | - |
| 0.2923 | 11850 | 0.0 | - |
| 0.2935 | 11900 | 0.0 | - |
| 0.2948 | 11950 | 0.0 | - |
| 0.2960 | 12000 | 0.0 | - |
| 0.2972 | 12050 | 0.0 | - |
| 0.2985 | 12100 | 0.0 | - |
| 0.2997 | 12150 | 0.0 | - |
| 0.3009 | 12200 | 0.0001 | - |
| 0.3022 | 12250 | 0.0 | - |
| 0.3034 | 12300 | 0.0 | - |
| 0.3046 | 12350 | 0.0 | - |
| 0.3059 | 12400 | 0.0 | - |
| 0.3071 | 12450 | 0.0 | - |
| 0.3083 | 12500 | 0.0 | - |
| 0.3096 | 12550 | 0.0 | - |
| 0.3108 | 12600 | 0.0 | - |
| 0.3120 | 12650 | 0.0 | - |
| 0.3133 | 12700 | 0.0 | - |
| 0.3145 | 12750 | 0.0 | - |
| 0.3157 | 12800 | 0.0 | - |
| 0.3170 | 12850 | 0.0 | - |
| 0.3182 | 12900 | 0.0 | - |
| 0.3194 | 12950 | 0.0 | - |
| 0.3207 | 13000 | 0.0 | - |
| 0.3219 | 13050 | 0.0001 | - |
| 0.3231 | 13100 | 0.0 | - |
| 0.3244 | 13150 | 0.0 | - |
| 0.3256 | 13200 | 0.0 | - |
| 0.3268 | 13250 | 0.0 | - |
| 0.3281 | 13300 | 0.0 | - |
| 0.3293 | 13350 | 0.0 | - |
| 0.3305 | 13400 | 0.0 | - |
| 0.3318 | 13450 | 0.0 | - |
| 0.3330 | 13500 | 0.0 | - |
| 0.3342 | 13550 | 0.0 | - |
| 0.3355 | 13600 | 0.0 | - |
| 0.3367 | 13650 | 0.0 | - |
| 0.3379 | 13700 | 0.0 | - |
| 0.3392 | 13750 | 0.0 | - |
| 0.3404 | 13800 | 0.0 | - |
| 0.3416 | 13850 | 0.0 | - |
| 0.3429 | 13900 | 0.0 | - |
| 0.3441 | 13950 | 0.0 | - |
| 0.3453 | 14000 | 0.0 | - |
| 0.3466 | 14050 | 0.0 | - |
| 0.3478 | 14100 | 0.0 | - |
| 0.3490 | 14150 | 0.0 | - |
| 0.3503 | 14200 | 0.0 | - |
| 0.3515 | 14250 | 0.0 | - |
| 0.3527 | 14300 | 0.0 | - |
| 0.3540 | 14350 | 0.0 | - |
| 0.3552 | 14400 | 0.0001 | - |
| 0.3564 | 14450 | 0.0 | - |
| 0.3577 | 14500 | 0.0 | - |
| 0.3589 | 14550 | 0.0 | - |
| 0.3601 | 14600 | 0.0 | - |
| 0.3614 | 14650 | 0.0 | - |
| 0.3626 | 14700 | 0.0 | - |
| 0.3638 | 14750 | 0.0 | - |
| 0.3651 | 14800 | 0.0 | - |
| 0.3663 | 14850 | 0.0 | - |
| 0.3675 | 14900 | 0.0 | - |
| 0.3688 | 14950 | 0.0 | - |
| 0.3700 | 15000 | 0.0 | - |
| 0.3712 | 15050 | 0.0 | - |
| 0.3725 | 15100 | 0.0 | - |
| 0.3737 | 15150 | 0.0 | - |
| 0.3749 | 15200 | 0.0 | - |
| 0.3762 | 15250 | 0.0 | - |
| 0.3774 | 15300 | 0.0 | - |
| 0.3786 | 15350 | 0.0 | - |
| 0.3799 | 15400 | 0.0 | - |
| 0.3811 | 15450 | 0.0 | - |
| 0.3823 | 15500 | 0.0 | - |
| 0.3836 | 15550 | 0.0 | - |
| 0.3848 | 15600 | 0.0 | - |
| 0.3860 | 15650 | 0.0 | - |
| 0.3873 | 15700 | 0.0 | - |
| 0.3885 | 15750 | 0.0 | - |
| 0.3897 | 15800 | 0.0001 | - |
| 0.3910 | 15850 | 0.0 | - |
| 0.3922 | 15900 | 0.0 | - |
| 0.3934 | 15950 | 0.0 | - |
| 0.3947 | 16000 | 0.0 | - |
| 0.3959 | 16050 | 0.0 | - |
| 0.3971 | 16100 | 0.0 | - |
| 0.3984 | 16150 | 0.0 | - |
| 0.3996 | 16200 | 0.0 | - |
| 0.4008 | 16250 | 0.0 | - |
| 0.4021 | 16300 | 0.0 | - |
| 0.4033 | 16350 | 0.0 | - |
| 0.4045 | 16400 | 0.0 | - |
| 0.4058 | 16450 | 0.0001 | - |
| 0.4070 | 16500 | 0.0 | - |
| 0.4082 | 16550 | 0.0 | - |
| 0.4095 | 16600 | 0.0 | - |
| 0.4107 | 16650 | 0.0 | - |
| 0.4119 | 16700 | 0.0 | - |
| 0.4132 | 16750 | 0.0 | - |
| 0.4144 | 16800 | 0.0001 | - |
| 0.4156 | 16850 | 0.0 | - |
| 0.4169 | 16900 | 0.0 | - |
| 0.4181 | 16950 | 0.0 | - |
| 0.4193 | 17000 | 0.0 | - |
| 0.4206 | 17050 | 0.0 | - |
| 0.4218 | 17100 | 0.0 | - |
| 0.4230 | 17150 | 0.0 | - |
| 0.4243 | 17200 | 0.0 | - |
| 0.4255 | 17250 | 0.0 | - |
| 0.4267 | 17300 | 0.0 | - |
| 0.4280 | 17350 | 0.0 | - |
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| 0.9990 | 40500 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.1
- PyTorch: 2.1.0+cu121
- Datasets: 2.18.0
- 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}
}
```