---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: man, product/whatever is my new best friend. i like product but the integration
of product into office and product is a lot of fun. i just spent the day feeding
it my training presentation i'm preparing in my day job and it was very helpful.
almost better than humans.
- text: that's great news! product is the perfect platform to share these advanced
product prompts and help more users get the most out of it!
- text: after only one week's trial of the new product with brand enabled, i have
replaced my default browser product that i was using for more than 7 years with
new product. i no longer need to spend a lot of time finding answers from a bunch
of search results and web pages. it's amazing
- text: very impressive. brand is finally fighting back. i am just a little worried
about the scalability of such a high context window size, since even in their
demos it took quite a while to process everything. regardless, i am very interested
in seeing what types of capabilities a >1m token size window can unleash.
- text: product the way it shows the sources is so fucking cool, this new ai is amazing
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.964
name: Accuracy
- type: f1
value:
- 0.9130434782608695
- 0.888888888888889
- 0.9779951100244498
name: F1
- type: precision
value:
- 0.9545454545454546
- 1.0
- 0.9615384615384616
name: Precision
- type: recall
value:
- 0.875
- 0.8
- 0.9950248756218906
name: Recall
---
# SetFit with BAAI/bge-small-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-small-en-v1.5](https://huggingface.co./BAAI/bge-small-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-small-en-v1.5](https://huggingface.co./BAAI/bge-small-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 | - 'i asked brand to write it and then let it translate back. so in reality i have no clue what i am sending...'
- "i saw someone summarize brand the other day; it doesn't give answers, it gives answer-shaped responses."
- 'thank you comrade i mean colleague. i will have brand summarize.'
|
| peak | - 'brand!! it helped me finish my resume. i just asked it if it could write my resume based on horribly written descriptions i came up with. and it made it all pretty:)'
- 'been building products for a bit now and your product (audio pen) is simple, useful and just works (like the early magic when product came out). congratulations and keep the flag flying high. not surprised that india is producing apps like yours. high time:-)'
- 'just got access to personalization in brand!! totally unexpected. very happy'
|
| pit | - 'brand recently i came across a very unwell patient in a psychiatric unit who was using product & this was reinforcing his delusional state & detrimentally impacting his mental health. anyone looking into this type of usage of product? what safe guards are being put in place?'
- 'brand product is def better at extracting numbers from images, product failed (pro version) twice...'
- "the stuff brand gives is entirely too scripted *and* impractical, which is what i'm trying to avoid:/"
|
## Evaluation
### Metrics
| Label | Accuracy | F1 | Precision | Recall |
|:--------|:---------|:------------------------------------------------------------|:----------------------------------------------|:---------------------------------|
| **all** | 0.964 | [0.9130434782608695, 0.888888888888889, 0.9779951100244498] | [0.9545454545454546, 1.0, 0.9615384615384616] | [0.875, 0.8, 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_v4")
# Run inference
preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 31.6606 | 98 |
| Label | Training Sample Count |
|:--------|:----------------------|
| pit | 277 |
| peak | 265 |
| neither | 1105 |
### 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2683 | - |
| 0.0012 | 50 | 0.2643 | - |
| 0.0023 | 100 | 0.2432 | - |
| 0.0035 | 150 | 0.2623 | - |
| 0.0047 | 200 | 0.2527 | - |
| 0.0058 | 250 | 0.2252 | - |
| 0.0070 | 300 | 0.2362 | - |
| 0.0082 | 350 | 0.2334 | - |
| 0.0093 | 400 | 0.2189 | - |
| 0.0105 | 450 | 0.2144 | - |
| 0.0117 | 500 | 0.1971 | - |
| 0.0129 | 550 | 0.1565 | - |
| 0.0140 | 600 | 0.0816 | - |
| 0.0152 | 650 | 0.1417 | - |
| 0.0164 | 700 | 0.1051 | - |
| 0.0175 | 750 | 0.0686 | - |
| 0.0187 | 800 | 0.0394 | - |
| 0.0199 | 850 | 0.0947 | - |
| 0.0210 | 900 | 0.0468 | - |
| 0.0222 | 950 | 0.0143 | - |
| 0.0234 | 1000 | 0.0281 | - |
| 0.0245 | 1050 | 0.0329 | - |
| 0.0257 | 1100 | 0.0206 | - |
| 0.0269 | 1150 | 0.0113 | - |
| 0.0280 | 1200 | 0.0054 | - |
| 0.0292 | 1250 | 0.0056 | - |
| 0.0304 | 1300 | 0.0209 | - |
| 0.0315 | 1350 | 0.0064 | - |
| 0.0327 | 1400 | 0.0085 | - |
| 0.0339 | 1450 | 0.0025 | - |
| 0.0350 | 1500 | 0.0031 | - |
| 0.0362 | 1550 | 0.0024 | - |
| 0.0374 | 1600 | 0.0014 | - |
| 0.0386 | 1650 | 0.0019 | - |
| 0.0397 | 1700 | 0.0023 | - |
| 0.0409 | 1750 | 0.0014 | - |
| 0.0421 | 1800 | 0.002 | - |
| 0.0432 | 1850 | 0.001 | - |
| 0.0444 | 1900 | 0.001 | - |
| 0.0456 | 1950 | 0.0019 | - |
| 0.0467 | 2000 | 0.0017 | - |
| 0.0479 | 2050 | 0.001 | - |
| 0.0491 | 2100 | 0.0008 | - |
| 0.0502 | 2150 | 0.0011 | - |
| 0.0514 | 2200 | 0.0006 | - |
| 0.0526 | 2250 | 0.0012 | - |
| 0.0537 | 2300 | 0.0008 | - |
| 0.0549 | 2350 | 0.0014 | - |
| 0.0561 | 2400 | 0.0009 | - |
| 0.0572 | 2450 | 0.0009 | - |
| 0.0584 | 2500 | 0.001 | - |
| 0.0596 | 2550 | 0.0007 | - |
| 0.0607 | 2600 | 0.0007 | - |
| 0.0619 | 2650 | 0.0006 | - |
| 0.0631 | 2700 | 0.0004 | - |
| 0.0643 | 2750 | 0.0007 | - |
| 0.0654 | 2800 | 0.0005 | - |
| 0.0666 | 2850 | 0.0007 | - |
| 0.0678 | 2900 | 0.0007 | - |
| 0.0689 | 2950 | 0.0006 | - |
| 0.0701 | 3000 | 0.0005 | - |
| 0.0713 | 3050 | 0.0007 | - |
| 0.0724 | 3100 | 0.0008 | - |
| 0.0736 | 3150 | 0.0005 | - |
| 0.0748 | 3200 | 0.0005 | - |
| 0.0759 | 3250 | 0.0005 | - |
| 0.0771 | 3300 | 0.0006 | - |
| 0.0783 | 3350 | 0.0006 | - |
| 0.0794 | 3400 | 0.0006 | - |
| 0.0806 | 3450 | 0.0004 | - |
| 0.0818 | 3500 | 0.0005 | - |
| 0.0829 | 3550 | 0.0005 | - |
| 0.0841 | 3600 | 0.0005 | - |
| 0.0853 | 3650 | 0.0005 | - |
| 0.0864 | 3700 | 0.0006 | - |
| 0.0876 | 3750 | 0.0039 | - |
| 0.0888 | 3800 | 0.0004 | - |
| 0.0900 | 3850 | 0.0003 | - |
| 0.0911 | 3900 | 0.0004 | - |
| 0.0923 | 3950 | 0.0007 | - |
| 0.0935 | 4000 | 0.0003 | - |
| 0.0946 | 4050 | 0.0004 | - |
| 0.0958 | 4100 | 0.0003 | - |
| 0.0970 | 4150 | 0.0003 | - |
| 0.0981 | 4200 | 0.0004 | - |
| 0.0993 | 4250 | 0.0003 | - |
| 0.1005 | 4300 | 0.0004 | - |
| 0.1016 | 4350 | 0.0003 | - |
| 0.1028 | 4400 | 0.0004 | - |
| 0.1040 | 4450 | 0.0003 | - |
| 0.1051 | 4500 | 0.0004 | - |
| 0.1063 | 4550 | 0.0003 | - |
| 0.1075 | 4600 | 0.0003 | - |
| 0.1086 | 4650 | 0.0003 | - |
| 0.1098 | 4700 | 0.0003 | - |
| 0.1110 | 4750 | 0.0016 | - |
| 0.1121 | 4800 | 0.0003 | - |
| 0.1133 | 4850 | 0.0002 | - |
| 0.1145 | 4900 | 0.0003 | - |
| 0.1157 | 4950 | 0.0002 | - |
| 0.1168 | 5000 | 0.0003 | - |
| 0.1180 | 5050 | 0.0003 | - |
| 0.1192 | 5100 | 0.0003 | - |
| 0.1203 | 5150 | 0.0002 | - |
| 0.1215 | 5200 | 0.0003 | - |
| 0.1227 | 5250 | 0.0002 | - |
| 0.1238 | 5300 | 0.0178 | - |
| 0.1250 | 5350 | 0.0014 | - |
| 0.1262 | 5400 | 0.002 | - |
| 0.1273 | 5450 | 0.0002 | - |
| 0.1285 | 5500 | 0.0008 | - |
| 0.1297 | 5550 | 0.0003 | - |
| 0.1308 | 5600 | 0.0002 | - |
| 0.1320 | 5650 | 0.0002 | - |
| 0.1332 | 5700 | 0.0002 | - |
| 0.1343 | 5750 | 0.0003 | - |
| 0.1355 | 5800 | 0.0002 | - |
| 0.1367 | 5850 | 0.0003 | - |
| 0.1378 | 5900 | 0.0003 | - |
| 0.1390 | 5950 | 0.0002 | - |
| 0.1402 | 6000 | 0.0002 | - |
| 0.1414 | 6050 | 0.0002 | - |
| 0.1425 | 6100 | 0.0002 | - |
| 0.1437 | 6150 | 0.0002 | - |
| 0.1449 | 6200 | 0.0002 | - |
| 0.1460 | 6250 | 0.0019 | - |
| 0.1472 | 6300 | 0.0005 | - |
| 0.1484 | 6350 | 0.0002 | - |
| 0.1495 | 6400 | 0.0005 | - |
| 0.1507 | 6450 | 0.0003 | - |
| 0.1519 | 6500 | 0.0208 | - |
| 0.1530 | 6550 | 0.0003 | - |
| 0.1542 | 6600 | 0.0002 | - |
| 0.1554 | 6650 | 0.0002 | - |
| 0.1565 | 6700 | 0.0002 | - |
| 0.1577 | 6750 | 0.0002 | - |
| 0.1589 | 6800 | 0.0002 | - |
| 0.1600 | 6850 | 0.0002 | - |
| 0.1612 | 6900 | 0.0104 | - |
| 0.1624 | 6950 | 0.0001 | - |
| 0.1635 | 7000 | 0.0002 | - |
| 0.1647 | 7050 | 0.0002 | - |
| 0.1659 | 7100 | 0.0002 | - |
| 0.1671 | 7150 | 0.0001 | - |
| 0.1682 | 7200 | 0.0002 | - |
| 0.1694 | 7250 | 0.0002 | - |
| 0.1706 | 7300 | 0.0003 | - |
| 0.1717 | 7350 | 0.0002 | - |
| 0.1729 | 7400 | 0.0001 | - |
| 0.1741 | 7450 | 0.0001 | - |
| 0.1752 | 7500 | 0.0002 | - |
| 0.1764 | 7550 | 0.0004 | - |
| 0.1776 | 7600 | 0.0002 | - |
| 0.1787 | 7650 | 0.0005 | - |
| 0.1799 | 7700 | 0.0001 | - |
| 0.1811 | 7750 | 0.0002 | - |
| 0.1822 | 7800 | 0.0002 | - |
| 0.1834 | 7850 | 0.0001 | - |
| 0.1846 | 7900 | 0.0002 | - |
| 0.1857 | 7950 | 0.0002 | - |
| 0.1869 | 8000 | 0.0002 | - |
| 0.1881 | 8050 | 0.0001 | - |
| 0.1892 | 8100 | 0.0002 | - |
| 0.1904 | 8150 | 0.0001 | - |
| 0.1916 | 8200 | 0.0001 | - |
| 0.1928 | 8250 | 0.0001 | - |
| 0.1939 | 8300 | 0.0001 | - |
| 0.1951 | 8350 | 0.0001 | - |
| 0.1963 | 8400 | 0.0002 | - |
| 0.1974 | 8450 | 0.0002 | - |
| 0.1986 | 8500 | 0.0002 | - |
| 0.1998 | 8550 | 0.0002 | - |
| 0.2009 | 8600 | 0.0001 | - |
| 0.2021 | 8650 | 0.0001 | - |
| 0.2033 | 8700 | 0.0001 | - |
| 0.2044 | 8750 | 0.0001 | - |
| 0.2056 | 8800 | 0.0001 | - |
| 0.2068 | 8850 | 0.0001 | - |
| 0.2079 | 8900 | 0.0001 | - |
| 0.2091 | 8950 | 0.0001 | - |
| 0.2103 | 9000 | 0.0001 | - |
| 0.2114 | 9050 | 0.0001 | - |
| 0.2126 | 9100 | 0.0001 | - |
| 0.2138 | 9150 | 0.0001 | - |
| 0.2149 | 9200 | 0.0001 | - |
| 0.2161 | 9250 | 0.0002 | - |
| 0.2173 | 9300 | 0.0001 | - |
| 0.2185 | 9350 | 0.0002 | - |
| 0.2196 | 9400 | 0.0001 | - |
| 0.2208 | 9450 | 0.0001 | - |
| 0.2220 | 9500 | 0.0001 | - |
| 0.2231 | 9550 | 0.0001 | - |
| 0.2243 | 9600 | 0.0001 | - |
| 0.2255 | 9650 | 0.0002 | - |
| 0.2266 | 9700 | 0.0002 | - |
| 0.2278 | 9750 | 0.0001 | - |
| 0.2290 | 9800 | 0.0001 | - |
| 0.2301 | 9850 | 0.0002 | - |
| 0.2313 | 9900 | 0.0001 | - |
| 0.2325 | 9950 | 0.0001 | - |
| 0.2336 | 10000 | 0.0001 | - |
| 0.2348 | 10050 | 0.0001 | - |
| 0.2360 | 10100 | 0.0001 | - |
| 0.2371 | 10150 | 0.0001 | - |
| 0.2383 | 10200 | 0.0001 | - |
| 0.2395 | 10250 | 0.0001 | - |
| 0.2406 | 10300 | 0.0001 | - |
| 0.2418 | 10350 | 0.0001 | - |
| 0.2430 | 10400 | 0.0001 | - |
| 0.2442 | 10450 | 0.0001 | - |
| 0.2453 | 10500 | 0.0001 | - |
| 0.2465 | 10550 | 0.0001 | - |
| 0.2477 | 10600 | 0.0001 | - |
| 0.2488 | 10650 | 0.0001 | - |
| 0.2500 | 10700 | 0.0001 | - |
| 0.2512 | 10750 | 0.0001 | - |
| 0.2523 | 10800 | 0.0001 | - |
| 0.2535 | 10850 | 0.0001 | - |
| 0.2547 | 10900 | 0.0001 | - |
| 0.2558 | 10950 | 0.0001 | - |
| 0.2570 | 11000 | 0.0002 | - |
| 0.2582 | 11050 | 0.0001 | - |
| 0.2593 | 11100 | 0.0003 | - |
| 0.2605 | 11150 | 0.0001 | - |
| 0.2617 | 11200 | 0.0001 | - |
| 0.2628 | 11250 | 0.0001 | - |
| 0.2640 | 11300 | 0.0001 | - |
| 0.2652 | 11350 | 0.0001 | - |
| 0.2663 | 11400 | 0.0001 | - |
| 0.2675 | 11450 | 0.0001 | - |
| 0.2687 | 11500 | 0.0001 | - |
| 0.2699 | 11550 | 0.0001 | - |
| 0.2710 | 11600 | 0.0001 | - |
| 0.2722 | 11650 | 0.0001 | - |
| 0.2734 | 11700 | 0.0001 | - |
| 0.2745 | 11750 | 0.0001 | - |
| 0.2757 | 11800 | 0.0001 | - |
| 0.2769 | 11850 | 0.0001 | - |
| 0.2780 | 11900 | 0.0001 | - |
| 0.2792 | 11950 | 0.0001 | - |
| 0.2804 | 12000 | 0.0001 | - |
| 0.2815 | 12050 | 0.0001 | - |
| 0.2827 | 12100 | 0.0137 | - |
| 0.2839 | 12150 | 0.0001 | - |
| 0.2850 | 12200 | 0.0001 | - |
| 0.2862 | 12250 | 0.0001 | - |
| 0.2874 | 12300 | 0.0001 | - |
| 0.2885 | 12350 | 0.0001 | - |
| 0.2897 | 12400 | 0.0001 | - |
| 0.2909 | 12450 | 0.0001 | - |
| 0.2920 | 12500 | 0.0001 | - |
| 0.2932 | 12550 | 0.0001 | - |
| 0.2944 | 12600 | 0.0001 | - |
| 0.2956 | 12650 | 0.0001 | - |
| 0.2967 | 12700 | 0.0 | - |
| 0.2979 | 12750 | 0.0001 | - |
| 0.2991 | 12800 | 0.0001 | - |
| 0.3002 | 12850 | 0.0001 | - |
| 0.3014 | 12900 | 0.0001 | - |
| 0.3026 | 12950 | 0.0001 | - |
| 0.3037 | 13000 | 0.0001 | - |
| 0.3049 | 13050 | 0.0001 | - |
| 0.3061 | 13100 | 0.0001 | - |
| 0.3072 | 13150 | 0.0001 | - |
| 0.3084 | 13200 | 0.0001 | - |
| 0.3096 | 13250 | 0.0001 | - |
| 0.3107 | 13300 | 0.0001 | - |
| 0.3119 | 13350 | 0.0001 | - |
| 0.3131 | 13400 | 0.0001 | - |
| 0.3142 | 13450 | 0.0001 | - |
| 0.3154 | 13500 | 0.0001 | - |
| 0.3166 | 13550 | 0.0001 | - |
| 0.3177 | 13600 | 0.0001 | - |
| 0.3189 | 13650 | 0.0001 | - |
| 0.3201 | 13700 | 0.0001 | - |
| 0.3213 | 13750 | 0.0001 | - |
| 0.3224 | 13800 | 0.0001 | - |
| 0.3236 | 13850 | 0.0 | - |
| 0.3248 | 13900 | 0.0001 | - |
| 0.3259 | 13950 | 0.0001 | - |
| 0.3271 | 14000 | 0.0001 | - |
| 0.3283 | 14050 | 0.0002 | - |
| 0.3294 | 14100 | 0.0001 | - |
| 0.3306 | 14150 | 0.0001 | - |
| 0.3318 | 14200 | 0.0001 | - |
| 0.3329 | 14250 | 0.0001 | - |
| 0.3341 | 14300 | 0.0001 | - |
| 0.3353 | 14350 | 0.0001 | - |
| 0.3364 | 14400 | 0.0001 | - |
| 0.3376 | 14450 | 0.0001 | - |
| 0.3388 | 14500 | 0.0001 | - |
| 0.3399 | 14550 | 0.0001 | - |
| 0.3411 | 14600 | 0.0001 | - |
| 0.3423 | 14650 | 0.0001 | - |
| 0.3434 | 14700 | 0.0001 | - |
| 0.3446 | 14750 | 0.0001 | - |
| 0.3458 | 14800 | 0.0001 | - |
| 0.3470 | 14850 | 0.0001 | - |
| 0.3481 | 14900 | 0.0001 | - |
| 0.3493 | 14950 | 0.0 | - |
| 0.3505 | 15000 | 0.0001 | - |
| 0.3516 | 15050 | 0.0001 | - |
| 0.3528 | 15100 | 0.0 | - |
| 0.3540 | 15150 | 0.0001 | - |
| 0.3551 | 15200 | 0.0001 | - |
| 0.3563 | 15250 | 0.0001 | - |
| 0.3575 | 15300 | 0.0001 | - |
| 0.3586 | 15350 | 0.0001 | - |
| 0.3598 | 15400 | 0.0001 | - |
| 0.3610 | 15450 | 0.0001 | - |
| 0.3621 | 15500 | 0.0001 | - |
| 0.3633 | 15550 | 0.0001 | - |
| 0.3645 | 15600 | 0.0002 | - |
| 0.3656 | 15650 | 0.0001 | - |
| 0.3668 | 15700 | 0.0001 | - |
| 0.3680 | 15750 | 0.0001 | - |
| 0.3692 | 15800 | 0.0001 | - |
| 0.3703 | 15850 | 0.0001 | - |
| 0.3715 | 15900 | 0.0001 | - |
| 0.3727 | 15950 | 0.0 | - |
| 0.3738 | 16000 | 0.0 | - |
| 0.3750 | 16050 | 0.0 | - |
| 0.3762 | 16100 | 0.0 | - |
| 0.3773 | 16150 | 0.0001 | - |
| 0.3785 | 16200 | 0.0001 | - |
| 0.3797 | 16250 | 0.0001 | - |
| 0.3808 | 16300 | 0.0001 | - |
| 0.3820 | 16350 | 0.0001 | - |
| 0.3832 | 16400 | 0.0001 | - |
| 0.3843 | 16450 | 0.0 | - |
| 0.3855 | 16500 | 0.0001 | - |
| 0.3867 | 16550 | 0.0 | - |
| 0.3878 | 16600 | 0.0001 | - |
| 0.3890 | 16650 | 0.0001 | - |
| 0.3902 | 16700 | 0.0001 | - |
| 0.3913 | 16750 | 0.0001 | - |
| 0.3925 | 16800 | 0.0002 | - |
| 0.3937 | 16850 | 0.0002 | - |
| 0.3949 | 16900 | 0.0 | - |
| 0.3960 | 16950 | 0.0 | - |
| 0.3972 | 17000 | 0.0 | - |
| 0.3984 | 17050 | 0.0001 | - |
| 0.3995 | 17100 | 0.0001 | - |
| 0.4007 | 17150 | 0.0001 | - |
| 0.4019 | 17200 | 0.0001 | - |
| 0.4030 | 17250 | 0.0 | - |
| 0.4042 | 17300 | 0.0 | - |
| 0.4054 | 17350 | 0.0279 | - |
| 0.4065 | 17400 | 0.0 | - |
| 0.4077 | 17450 | 0.0 | - |
| 0.4089 | 17500 | 0.0 | - |
| 0.4100 | 17550 | 0.0 | - |
| 0.4112 | 17600 | 0.0001 | - |
| 0.4124 | 17650 | 0.0 | - |
| 0.4135 | 17700 | 0.028 | - |
| 0.4147 | 17750 | 0.0 | - |
| 0.4159 | 17800 | 0.0 | - |
| 0.4170 | 17850 | 0.0 | - |
| 0.4182 | 17900 | 0.0 | - |
| 0.4194 | 17950 | 0.0001 | - |
| 0.4206 | 18000 | 0.0 | - |
| 0.4217 | 18050 | 0.0 | - |
| 0.4229 | 18100 | 0.0001 | - |
| 0.4241 | 18150 | 0.0 | - |
| 0.4252 | 18200 | 0.0 | - |
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| 0.9416 | 40300 | 0.0001 | - |
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| 0.9836 | 42100 | 0.0 | - |
| 0.9848 | 42150 | 0.0 | - |
| 0.9860 | 42200 | 0.0 | - |
| 0.9871 | 42250 | 0.0 | - |
| 0.9883 | 42300 | 0.0 | - |
| 0.9895 | 42350 | 0.0 | - |
| 0.9906 | 42400 | 0.0 | - |
| 0.9918 | 42450 | 0.0 | - |
| 0.9930 | 42500 | 0.0 | - |
| 0.9941 | 42550 | 0.0 | - |
| 0.9953 | 42600 | 0.0 | - |
| 0.9965 | 42650 | 0.0 | - |
| 0.9976 | 42700 | 0.0 | - |
| 0.9988 | 42750 | 0.0 | - |
| 1.0000 | 42800 | 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}
}
```