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---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: book:I was hooked!! Garth Nix is a awesome writer and though the book is a
little babyish - its definetly worth a read! I thought the whole minute - hand
- is - a - key part was a real good idea plus the names are so fun! The only thing
I didn't like was that Arthur doesen't take his rightful place as "Monday"
- text: book:The lawyer says he tracked Jack from his book and would like Jack to
investigate the brutal murders of thirty-seven year old Gina Anderson and her
son Joshua in their Seattle home; the house was trashed and the husband a lecturer
at the nearby community college vanished
- text: beings:Arthur with the lesser key to the lower kingdom of the House in hand,
must travel into the House to find a cure for the mysterious plague that is striking
the people of his town and his loved ones and find out why there are beings intent
on getting the key from him, even if it means killing him
- text: figures:But when a fight emerges between the two figures - Mister Monday and
Sneezer - they both disappear without any further regard to Arthur
- text: book:I could not put this book down if my life depended on it! I have never
in my life read a book this fast
inference: false
---
# SetFit Aspect Model with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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. 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:** [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
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [omymble/books-full-bge-aspect](https://huggingface.co/omymble/books-full-bge-aspect)
- **SetFitABSA Polarity Model:** [omymble/books-full-bge-polarity](https://huggingface.co/omymble/books-full-bge-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | <ul><li>"younger ones:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>'Nix:-enjoy the genre of fantasies, of a unknown world, as Nix weaves a wonderful tale of the things that will open your eyes to a different world'</li><li>'mystery:The mystery is secondary to the rest of the story and is only really approached in the remaining 30 pages of the book'</li></ul> |
| no aspect | <ul><li>"point:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>"discussion:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>"child:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li></ul> |
## 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(
"omymble/books-full-bge-aspect",
"omymble/books-full-bge-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 25.9648 | 72 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 572 |
| aspect | 167 |
### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (5, 5)
- 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: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.2687 | - |
| 0.0090 | 50 | 0.2516 | - |
| 0.0180 | 100 | 0.2619 | - |
| 0.0270 | 150 | 0.2499 | - |
| 0.0360 | 200 | 0.2428 | - |
| 0.0450 | 250 | 0.2443 | - |
| 0.0540 | 300 | 0.246 | - |
| 0.0629 | 350 | 0.249 | - |
| 0.0719 | 400 | 0.2354 | - |
| 0.0809 | 450 | 0.2347 | - |
| 0.0899 | 500 | 0.2154 | - |
| 0.0989 | 550 | 0.2285 | - |
| 0.1079 | 600 | 0.1812 | - |
| 0.1169 | 650 | 0.1446 | - |
| 0.1259 | 700 | 0.165 | - |
| 0.1349 | 750 | 0.1125 | - |
| 0.1439 | 800 | 0.0971 | - |
| 0.1529 | 850 | 0.1059 | - |
| 0.1619 | 900 | 0.0866 | - |
| 0.1709 | 950 | 0.0492 | - |
| **0.1799** | **1000** | **0.0546** | **0.274** |
| 0.1888 | 1050 | 0.037 | - |
| 0.1978 | 1100 | 0.0189 | - |
| 0.2068 | 1150 | 0.0279 | - |
| 0.2158 | 1200 | 0.004 | - |
| 0.2248 | 1250 | 0.0309 | - |
| 0.2338 | 1300 | 0.0049 | - |
| 0.2428 | 1350 | 0.0286 | - |
| 0.2518 | 1400 | 0.0234 | - |
| 0.2608 | 1450 | 0.0158 | - |
| 0.2698 | 1500 | 0.0354 | - |
| 0.2788 | 1550 | 0.0062 | - |
| 0.2878 | 1600 | 0.0172 | - |
| 0.2968 | 1650 | 0.0389 | - |
| 0.3058 | 1700 | 0.0221 | - |
| 0.3147 | 1750 | 0.0065 | - |
| 0.3237 | 1800 | 0.0128 | - |
| 0.3327 | 1850 | 0.0225 | - |
| 0.3417 | 1900 | 0.0021 | - |
| 0.3507 | 1950 | 0.0102 | - |
| 0.3597 | 2000 | 0.012 | 0.3429 |
| 0.3687 | 2050 | 0.0249 | - |
| 0.3777 | 2100 | 0.0054 | - |
| 0.3867 | 2150 | 0.0014 | - |
| 0.3957 | 2200 | 0.0014 | - |
| 0.4047 | 2250 | 0.0143 | - |
| 0.4137 | 2300 | 0.0078 | - |
| 0.4227 | 2350 | 0.0195 | - |
| 0.4317 | 2400 | 0.0006 | - |
| 0.4406 | 2450 | 0.0014 | - |
| 0.4496 | 2500 | 0.0083 | - |
| 0.4586 | 2550 | 0.0141 | - |
| 0.4676 | 2600 | 0.0046 | - |
| 0.4766 | 2650 | 0.01 | - |
| 0.4856 | 2700 | 0.0268 | - |
| 0.4946 | 2750 | 0.0008 | - |
| 0.5036 | 2800 | 0.0076 | - |
| 0.5126 | 2850 | 0.0004 | - |
| 0.5216 | 2900 | 0.0037 | - |
| 0.5306 | 2950 | 0.0005 | - |
| 0.5396 | 3000 | 0.0065 | 0.3565 |
| 0.5486 | 3050 | 0.002 | - |
| 0.5576 | 3100 | 0.0072 | - |
| 0.5665 | 3150 | 0.0141 | - |
| 0.5755 | 3200 | 0.0004 | - |
| 0.5845 | 3250 | 0.0086 | - |
| 0.5935 | 3300 | 0.0098 | - |
| 0.6025 | 3350 | 0.0048 | - |
| 0.6115 | 3400 | 0.0013 | - |
| 0.6205 | 3450 | 0.007 | - |
| 0.6295 | 3500 | 0.0059 | - |
| 0.6385 | 3550 | 0.0174 | - |
| 0.6475 | 3600 | 0.0003 | - |
| 0.6565 | 3650 | 0.0004 | - |
| 0.6655 | 3700 | 0.0032 | - |
| 0.6745 | 3750 | 0.0004 | - |
| 0.6835 | 3800 | 0.0035 | - |
| 0.6924 | 3850 | 0.0019 | - |
| 0.7014 | 3900 | 0.015 | - |
| 0.7104 | 3950 | 0.0204 | - |
| 0.7194 | 4000 | 0.0016 | 0.3404 |
| 0.7284 | 4050 | 0.0003 | - |
| 0.7374 | 4100 | 0.0036 | - |
| 0.7464 | 4150 | 0.0016 | - |
| 0.7554 | 4200 | 0.0104 | - |
| 0.7644 | 4250 | 0.003 | - |
| 0.7734 | 4300 | 0.0159 | - |
| 0.7824 | 4350 | 0.0029 | - |
| 0.7914 | 4400 | 0.0068 | - |
| 0.8004 | 4450 | 0.0021 | - |
| 0.8094 | 4500 | 0.006 | - |
| 0.8183 | 4550 | 0.006 | - |
| 0.8273 | 4600 | 0.0038 | - |
| 0.8363 | 4650 | 0.008 | - |
| 0.8453 | 4700 | 0.0003 | - |
| 0.8543 | 4750 | 0.0126 | - |
| 0.8633 | 4800 | 0.0002 | - |
| 0.8723 | 4850 | 0.0041 | - |
| 0.8813 | 4900 | 0.0002 | - |
| 0.8903 | 4950 | 0.0137 | - |
| 0.8993 | 5000 | 0.0041 | 0.3363 |
| 0.9083 | 5050 | 0.0252 | - |
| 0.9173 | 5100 | 0.0023 | - |
| 0.9263 | 5150 | 0.0062 | - |
| 0.9353 | 5200 | 0.0152 | - |
| 0.9442 | 5250 | 0.0014 | - |
| 0.9532 | 5300 | 0.0224 | - |
| 0.9622 | 5350 | 0.0174 | - |
| 0.9712 | 5400 | 0.0066 | - |
| 0.9802 | 5450 | 0.0002 | - |
| 0.9892 | 5500 | 0.0136 | - |
| 0.9982 | 5550 | 0.0036 | - |
| 1.0072 | 5600 | 0.0102 | - |
| 1.0162 | 5650 | 0.011 | - |
| 1.0252 | 5700 | 0.0035 | - |
| 1.0342 | 5750 | 0.0002 | - |
| 1.0432 | 5800 | 0.0002 | - |
| 1.0522 | 5850 | 0.0044 | - |
| 1.0612 | 5900 | 0.0125 | - |
| 1.0701 | 5950 | 0.0061 | - |
| 1.0791 | 6000 | 0.0165 | 0.3591 |
| 1.0881 | 6050 | 0.006 | - |
| 1.0971 | 6100 | 0.0003 | - |
| 1.1061 | 6150 | 0.0074 | - |
| 1.1151 | 6200 | 0.0019 | - |
| 1.1241 | 6250 | 0.0002 | - |
| 1.1331 | 6300 | 0.0064 | - |
| 1.1421 | 6350 | 0.0127 | - |
| 1.1511 | 6400 | 0.0012 | - |
| 1.1601 | 6450 | 0.0003 | - |
| 1.1691 | 6500 | 0.0251 | - |
| 1.1781 | 6550 | 0.0002 | - |
| 1.1871 | 6600 | 0.0003 | - |
| 1.1960 | 6650 | 0.0002 | - |
| 1.2050 | 6700 | 0.0002 | - |
| 1.2140 | 6750 | 0.0123 | - |
| 1.2230 | 6800 | 0.0055 | - |
| 1.2320 | 6850 | 0.0098 | - |
| 1.2410 | 6900 | 0.0028 | - |
| 1.25 | 6950 | 0.0049 | - |
| 1.2590 | 7000 | 0.0021 | 0.3537 |
| 1.2680 | 7050 | 0.0147 | - |
| 1.2770 | 7100 | 0.003 | - |
| 1.2860 | 7150 | 0.0002 | - |
| 1.2950 | 7200 | 0.0049 | - |
| 1.3040 | 7250 | 0.0033 | - |
| 1.3129 | 7300 | 0.0002 | - |
| 1.3219 | 7350 | 0.0065 | - |
| 1.3309 | 7400 | 0.0043 | - |
| 1.3399 | 7450 | 0.0107 | - |
| 1.3489 | 7500 | 0.0184 | - |
| 1.3579 | 7550 | 0.0116 | - |
| 1.3669 | 7600 | 0.0041 | - |
| 1.3759 | 7650 | 0.0001 | - |
| 1.3849 | 7700 | 0.0001 | - |
| 1.3939 | 7750 | 0.0074 | - |
| 1.4029 | 7800 | 0.0002 | - |
| 1.4119 | 7850 | 0.0087 | - |
| 1.4209 | 7900 | 0.0014 | - |
| 1.4299 | 7950 | 0.0045 | - |
| 1.4388 | 8000 | 0.0018 | 0.3439 |
| 1.4478 | 8050 | 0.0039 | - |
| 1.4568 | 8100 | 0.007 | - |
| 1.4658 | 8150 | 0.0066 | - |
| 1.4748 | 8200 | 0.0101 | - |
| 1.4838 | 8250 | 0.0047 | - |
| 1.4928 | 8300 | 0.0021 | - |
| 1.5018 | 8350 | 0.0002 | - |
| 1.5108 | 8400 | 0.0116 | - |
| 1.5198 | 8450 | 0.0017 | - |
| 1.5288 | 8500 | 0.0032 | - |
| 1.5378 | 8550 | 0.0053 | - |
| 1.5468 | 8600 | 0.0038 | - |
| 1.5558 | 8650 | 0.0001 | - |
| 1.5647 | 8700 | 0.002 | - |
| 1.5737 | 8750 | 0.0065 | - |
| 1.5827 | 8800 | 0.0064 | - |
| 1.5917 | 8850 | 0.0001 | - |
| 1.6007 | 8900 | 0.0049 | - |
| 1.6097 | 8950 | 0.0002 | - |
| 1.6187 | 9000 | 0.0083 | 0.3486 |
| 1.6277 | 9050 | 0.0105 | - |
| 1.6367 | 9100 | 0.0019 | - |
| 1.6457 | 9150 | 0.0002 | - |
| 1.6547 | 9200 | 0.0049 | - |
| 1.6637 | 9250 | 0.0001 | - |
| 1.6727 | 9300 | 0.0097 | - |
| 1.6817 | 9350 | 0.0098 | - |
| 1.6906 | 9400 | 0.0022 | - |
| 1.6996 | 9450 | 0.0142 | - |
| 1.7086 | 9500 | 0.0025 | - |
| 1.7176 | 9550 | 0.0147 | - |
| 1.7266 | 9600 | 0.0086 | - |
| 1.7356 | 9650 | 0.0062 | - |
| 1.7446 | 9700 | 0.0002 | - |
| 1.7536 | 9750 | 0.0103 | - |
| 1.7626 | 9800 | 0.0186 | - |
| 1.7716 | 9850 | 0.0112 | - |
| 1.7806 | 9900 | 0.0042 | - |
| 1.7896 | 9950 | 0.0166 | - |
| 1.7986 | 10000 | 0.0002 | 0.3571 |
| 1.8076 | 10050 | 0.0029 | - |
| 1.8165 | 10100 | 0.0055 | - |
| 1.8255 | 10150 | 0.0057 | - |
| 1.8345 | 10200 | 0.0163 | - |
| 1.8435 | 10250 | 0.0093 | - |
| 1.8525 | 10300 | 0.0083 | - |
| 1.8615 | 10350 | 0.0073 | - |
| 1.8705 | 10400 | 0.0089 | - |
| 1.8795 | 10450 | 0.0068 | - |
| 1.8885 | 10500 | 0.0001 | - |
| 1.8975 | 10550 | 0.0232 | - |
| 1.9065 | 10600 | 0.0161 | - |
| 1.9155 | 10650 | 0.0088 | - |
| 1.9245 | 10700 | 0.0002 | - |
| 1.9335 | 10750 | 0.0093 | - |
| 1.9424 | 10800 | 0.0103 | - |
| 1.9514 | 10850 | 0.002 | - |
| 1.9604 | 10900 | 0.0113 | - |
| 1.9694 | 10950 | 0.0055 | - |
| 1.9784 | 11000 | 0.0148 | 0.3461 |
| 1.9874 | 11050 | 0.0001 | - |
| 1.9964 | 11100 | 0.0017 | - |
| 2.0054 | 11150 | 0.0001 | - |
| 2.0144 | 11200 | 0.0204 | - |
| 2.0234 | 11250 | 0.0032 | - |
| 2.0324 | 11300 | 0.0029 | - |
| 2.0414 | 11350 | 0.002 | - |
| 2.0504 | 11400 | 0.0001 | - |
| 2.0594 | 11450 | 0.005 | - |
| 2.0683 | 11500 | 0.0001 | - |
| 2.0773 | 11550 | 0.0051 | - |
| 2.0863 | 11600 | 0.0095 | - |
| 2.0953 | 11650 | 0.0093 | - |
| 2.1043 | 11700 | 0.0171 | - |
| 2.1133 | 11750 | 0.0059 | - |
| 2.1223 | 11800 | 0.0026 | - |
| 2.1313 | 11850 | 0.0092 | - |
| 2.1403 | 11900 | 0.0002 | - |
| 2.1493 | 11950 | 0.0069 | - |
| 2.1583 | 12000 | 0.006 | 0.3572 |
| 2.1673 | 12050 | 0.009 | - |
| 2.1763 | 12100 | 0.008 | - |
| 2.1853 | 12150 | 0.0001 | - |
| 2.1942 | 12200 | 0.0062 | - |
| 2.2032 | 12250 | 0.0086 | - |
| 2.2122 | 12300 | 0.0001 | - |
| 2.2212 | 12350 | 0.0001 | - |
| 2.2302 | 12400 | 0.0001 | - |
| 2.2392 | 12450 | 0.0001 | - |
| 2.2482 | 12500 | 0.0022 | - |
| 2.2572 | 12550 | 0.0014 | - |
| 2.2662 | 12600 | 0.0014 | - |
| 2.2752 | 12650 | 0.009 | - |
| 2.2842 | 12700 | 0.0001 | - |
| 2.2932 | 12750 | 0.0081 | - |
| 2.3022 | 12800 | 0.0127 | - |
| 2.3112 | 12850 | 0.0001 | - |
| 2.3201 | 12900 | 0.0028 | - |
| 2.3291 | 12950 | 0.0016 | - |
| 2.3381 | 13000 | 0.0051 | 0.3587 |
| 2.3471 | 13050 | 0.0044 | - |
| 2.3561 | 13100 | 0.0133 | - |
| 2.3651 | 13150 | 0.0043 | - |
| 2.3741 | 13200 | 0.0001 | - |
| 2.3831 | 13250 | 0.0017 | - |
| 2.3921 | 13300 | 0.0095 | - |
| 2.4011 | 13350 | 0.008 | - |
| 2.4101 | 13400 | 0.0074 | - |
| 2.4191 | 13450 | 0.0181 | - |
| 2.4281 | 13500 | 0.0141 | - |
| 2.4371 | 13550 | 0.0114 | - |
| 2.4460 | 13600 | 0.0046 | - |
| 2.4550 | 13650 | 0.0053 | - |
| 2.4640 | 13700 | 0.0001 | - |
| 2.4730 | 13750 | 0.0001 | - |
| 2.4820 | 13800 | 0.0114 | - |
| 2.4910 | 13850 | 0.0001 | - |
| 2.5 | 13900 | 0.0075 | - |
| 2.5090 | 13950 | 0.0016 | - |
| 2.5180 | 14000 | 0.0014 | 0.3376 |
| 2.5270 | 14050 | 0.0075 | - |
| 2.5360 | 14100 | 0.0001 | - |
| 2.5450 | 14150 | 0.0001 | - |
| 2.5540 | 14200 | 0.0013 | - |
| 2.5629 | 14250 | 0.0001 | - |
| 2.5719 | 14300 | 0.0082 | - |
| 2.5809 | 14350 | 0.0021 | - |
| 2.5899 | 14400 | 0.0001 | - |
| 2.5989 | 14450 | 0.0001 | - |
| 2.6079 | 14500 | 0.0016 | - |
| 2.6169 | 14550 | 0.0001 | - |
| 2.6259 | 14600 | 0.0001 | - |
| 2.6349 | 14650 | 0.0058 | - |
| 2.6439 | 14700 | 0.0223 | - |
| 2.6529 | 14750 | 0.0001 | - |
| 2.6619 | 14800 | 0.0001 | - |
| 2.6709 | 14850 | 0.0249 | - |
| 2.6799 | 14900 | 0.008 | - |
| 2.6888 | 14950 | 0.0071 | - |
| 2.6978 | 15000 | 0.0237 | 0.3769 |
| 2.7068 | 15050 | 0.0001 | - |
| 2.7158 | 15100 | 0.0016 | - |
| 2.7248 | 15150 | 0.0031 | - |
| 2.7338 | 15200 | 0.0063 | - |
| 2.7428 | 15250 | 0.0001 | - |
| 2.7518 | 15300 | 0.0127 | - |
| 2.7608 | 15350 | 0.0001 | - |
| 2.7698 | 15400 | 0.0114 | - |
| 2.7788 | 15450 | 0.0106 | - |
| 2.7878 | 15500 | 0.0086 | - |
| 2.7968 | 15550 | 0.0083 | - |
| 2.8058 | 15600 | 0.0001 | - |
| 2.8147 | 15650 | 0.0001 | - |
| 2.8237 | 15700 | 0.0035 | - |
| 2.8327 | 15750 | 0.0095 | - |
| 2.8417 | 15800 | 0.0041 | - |
| 2.8507 | 15850 | 0.0001 | - |
| 2.8597 | 15900 | 0.0001 | - |
| 2.8687 | 15950 | 0.0001 | - |
| 2.8777 | 16000 | 0.0001 | 0.3509 |
| 2.8867 | 16050 | 0.0001 | - |
| 2.8957 | 16100 | 0.0124 | - |
| 2.9047 | 16150 | 0.0083 | - |
| 2.9137 | 16200 | 0.0017 | - |
| 2.9227 | 16250 | 0.0001 | - |
| 2.9317 | 16300 | 0.0042 | - |
| 2.9406 | 16350 | 0.0058 | - |
| 2.9496 | 16400 | 0.0001 | - |
| 2.9586 | 16450 | 0.0001 | - |
| 2.9676 | 16500 | 0.0021 | - |
| 2.9766 | 16550 | 0.0025 | - |
| 2.9856 | 16600 | 0.0068 | - |
| 2.9946 | 16650 | 0.0099 | - |
| 3.0036 | 16700 | 0.0015 | - |
| 3.0126 | 16750 | 0.0086 | - |
| 3.0216 | 16800 | 0.0162 | - |
| 3.0306 | 16850 | 0.0001 | - |
| 3.0396 | 16900 | 0.0181 | - |
| 3.0486 | 16950 | 0.0083 | - |
| 3.0576 | 17000 | 0.0045 | 0.346 |
| 3.0665 | 17050 | 0.0072 | - |
| 3.0755 | 17100 | 0.0045 | - |
| 3.0845 | 17150 | 0.005 | - |
| 3.0935 | 17200 | 0.003 | - |
| 3.1025 | 17250 | 0.0069 | - |
| 3.1115 | 17300 | 0.0001 | - |
| 3.1205 | 17350 | 0.003 | - |
| 3.1295 | 17400 | 0.0077 | - |
| 3.1385 | 17450 | 0.0001 | - |
| 3.1475 | 17500 | 0.0001 | - |
| 3.1565 | 17550 | 0.0166 | - |
| 3.1655 | 17600 | 0.0001 | - |
| 3.1745 | 17650 | 0.0001 | - |
| 3.1835 | 17700 | 0.0084 | - |
| 3.1924 | 17750 | 0.0106 | - |
| 3.2014 | 17800 | 0.0027 | - |
| 3.2104 | 17850 | 0.0092 | - |
| 3.2194 | 17900 | 0.0001 | - |
| 3.2284 | 17950 | 0.0001 | - |
| 3.2374 | 18000 | 0.0066 | 0.3501 |
| 3.2464 | 18050 | 0.0037 | - |
| 3.2554 | 18100 | 0.0035 | - |
| 3.2644 | 18150 | 0.0029 | - |
| 3.2734 | 18200 | 0.0017 | - |
| 3.2824 | 18250 | 0.0001 | - |
| 3.2914 | 18300 | 0.0034 | - |
| 3.3004 | 18350 | 0.0121 | - |
| 3.3094 | 18400 | 0.0051 | - |
| 3.3183 | 18450 | 0.0024 | - |
| 3.3273 | 18500 | 0.0019 | - |
| 3.3363 | 18550 | 0.0014 | - |
| 3.3453 | 18600 | 0.0167 | - |
| 3.3543 | 18650 | 0.0097 | - |
| 3.3633 | 18700 | 0.0025 | - |
| 3.3723 | 18750 | 0.0065 | - |
| 3.3813 | 18800 | 0.011 | - |
| 3.3903 | 18850 | 0.0001 | - |
| 3.3993 | 18900 | 0.0001 | - |
| 3.4083 | 18950 | 0.0072 | - |
| 3.4173 | 19000 | 0.0132 | 0.3511 |
| 3.4263 | 19050 | 0.0084 | - |
| 3.4353 | 19100 | 0.0015 | - |
| 3.4442 | 19150 | 0.0014 | - |
| 3.4532 | 19200 | 0.011 | - |
| 3.4622 | 19250 | 0.0083 | - |
| 3.4712 | 19300 | 0.0073 | - |
| 3.4802 | 19350 | 0.0024 | - |
| 3.4892 | 19400 | 0.002 | - |
| 3.4982 | 19450 | 0.0155 | - |
| 3.5072 | 19500 | 0.0042 | - |
| 3.5162 | 19550 | 0.0001 | - |
| 3.5252 | 19600 | 0.0043 | - |
| 3.5342 | 19650 | 0.0026 | - |
| 3.5432 | 19700 | 0.0022 | - |
| 3.5522 | 19750 | 0.002 | - |
| 3.5612 | 19800 | 0.0018 | - |
| 3.5701 | 19850 | 0.0001 | - |
| 3.5791 | 19900 | 0.0012 | - |
| 3.5881 | 19950 | 0.002 | - |
| 3.5971 | 20000 | 0.0089 | 0.3516 |
| 3.6061 | 20050 | 0.003 | - |
| 3.6151 | 20100 | 0.0036 | - |
| 3.6241 | 20150 | 0.0001 | - |
| 3.6331 | 20200 | 0.0001 | - |
| 3.6421 | 20250 | 0.0156 | - |
| 3.6511 | 20300 | 0.0001 | - |
| 3.6601 | 20350 | 0.0174 | - |
| 3.6691 | 20400 | 0.0001 | - |
| 3.6781 | 20450 | 0.011 | - |
| 3.6871 | 20500 | 0.0001 | - |
| 3.6960 | 20550 | 0.0047 | - |
| 3.7050 | 20600 | 0.0132 | - |
| 3.7140 | 20650 | 0.007 | - |
| 3.7230 | 20700 | 0.0001 | - |
| 3.7320 | 20750 | 0.0025 | - |
| 3.7410 | 20800 | 0.0049 | - |
| 3.75 | 20850 | 0.0074 | - |
| 3.7590 | 20900 | 0.002 | - |
| 3.7680 | 20950 | 0.0112 | - |
| 3.7770 | 21000 | 0.0001 | 0.3483 |
| 3.7860 | 21050 | 0.0001 | - |
| 3.7950 | 21100 | 0.0064 | - |
| 3.8040 | 21150 | 0.0133 | - |
| 3.8129 | 21200 | 0.0001 | - |
| 3.8219 | 21250 | 0.0112 | - |
| 3.8309 | 21300 | 0.0001 | - |
| 3.8399 | 21350 | 0.0001 | - |
| 3.8489 | 21400 | 0.0001 | - |
| 3.8579 | 21450 | 0.0025 | - |
| 3.8669 | 21500 | 0.0047 | - |
| 3.8759 | 21550 | 0.0001 | - |
| 3.8849 | 21600 | 0.0062 | - |
| 3.8939 | 21650 | 0.0001 | - |
| 3.9029 | 21700 | 0.0315 | - |
| 3.9119 | 21750 | 0.002 | - |
| 3.9209 | 21800 | 0.0034 | - |
| 3.9299 | 21850 | 0.004 | - |
| 3.9388 | 21900 | 0.0046 | - |
| 3.9478 | 21950 | 0.008 | - |
| 3.9568 | 22000 | 0.0103 | 0.3474 |
| 3.9658 | 22050 | 0.0142 | - |
| 3.9748 | 22100 | 0.0207 | - |
| 3.9838 | 22150 | 0.0105 | - |
| 3.9928 | 22200 | 0.0114 | - |
| 4.0018 | 22250 | 0.002 | - |
| 4.0108 | 22300 | 0.0121 | - |
| 4.0198 | 22350 | 0.0001 | - |
| 4.0288 | 22400 | 0.0058 | - |
| 4.0378 | 22450 | 0.0045 | - |
| 4.0468 | 22500 | 0.0001 | - |
| 4.0558 | 22550 | 0.0086 | - |
| 4.0647 | 22600 | 0.0121 | - |
| 4.0737 | 22650 | 0.0045 | - |
| 4.0827 | 22700 | 0.0001 | - |
| 4.0917 | 22750 | 0.0046 | - |
| 4.1007 | 22800 | 0.0076 | - |
| 4.1097 | 22850 | 0.0001 | - |
| 4.1187 | 22900 | 0.0154 | - |
| 4.1277 | 22950 | 0.0108 | - |
| 4.1367 | 23000 | 0.0058 | 0.3575 |
| 4.1457 | 23050 | 0.0088 | - |
| 4.1547 | 23100 | 0.0019 | - |
| 4.1637 | 23150 | 0.0055 | - |
| 4.1727 | 23200 | 0.0299 | - |
| 4.1817 | 23250 | 0.0085 | - |
| 4.1906 | 23300 | 0.0016 | - |
| 4.1996 | 23350 | 0.0001 | - |
| 4.2086 | 23400 | 0.0001 | - |
| 4.2176 | 23450 | 0.0072 | - |
| 4.2266 | 23500 | 0.0092 | - |
| 4.2356 | 23550 | 0.0001 | - |
| 4.2446 | 23600 | 0.0064 | - |
| 4.2536 | 23650 | 0.0065 | - |
| 4.2626 | 23700 | 0.0001 | - |
| 4.2716 | 23750 | 0.0017 | - |
| 4.2806 | 23800 | 0.0083 | - |
| 4.2896 | 23850 | 0.0001 | - |
| 4.2986 | 23900 | 0.0039 | - |
| 4.3076 | 23950 | 0.002 | - |
| 4.3165 | 24000 | 0.0037 | 0.357 |
| 4.3255 | 24050 | 0.0095 | - |
| 4.3345 | 24100 | 0.002 | - |
| 4.3435 | 24150 | 0.017 | - |
| 4.3525 | 24200 | 0.0086 | - |
| 4.3615 | 24250 | 0.007 | - |
| 4.3705 | 24300 | 0.0023 | - |
| 4.3795 | 24350 | 0.0122 | - |
| 4.3885 | 24400 | 0.0097 | - |
| 4.3975 | 24450 | 0.0027 | - |
| 4.4065 | 24500 | 0.0081 | - |
| 4.4155 | 24550 | 0.0043 | - |
| 4.4245 | 24600 | 0.0055 | - |
| 4.4335 | 24650 | 0.0001 | - |
| 4.4424 | 24700 | 0.0014 | - |
| 4.4514 | 24750 | 0.0001 | - |
| 4.4604 | 24800 | 0.0091 | - |
| 4.4694 | 24850 | 0.0087 | - |
| 4.4784 | 24900 | 0.0101 | - |
| 4.4874 | 24950 | 0.0001 | - |
| 4.4964 | 25000 | 0.013 | 0.3566 |
| 4.5054 | 25050 | 0.013 | - |
| 4.5144 | 25100 | 0.0082 | - |
| 4.5234 | 25150 | 0.0063 | - |
| 4.5324 | 25200 | 0.0046 | - |
| 4.5414 | 25250 | 0.0087 | - |
| 4.5504 | 25300 | 0.0063 | - |
| 4.5594 | 25350 | 0.0019 | - |
| 4.5683 | 25400 | 0.0061 | - |
| 4.5773 | 25450 | 0.004 | - |
| 4.5863 | 25500 | 0.0001 | - |
| 4.5953 | 25550 | 0.0001 | - |
| 4.6043 | 25600 | 0.0088 | - |
| 4.6133 | 25650 | 0.0191 | - |
| 4.6223 | 25700 | 0.0124 | - |
| 4.6313 | 25750 | 0.0001 | - |
| 4.6403 | 25800 | 0.0023 | - |
| 4.6493 | 25850 | 0.0001 | - |
| 4.6583 | 25900 | 0.0068 | - |
| 4.6673 | 25950 | 0.0001 | - |
| 4.6763 | 26000 | 0.0034 | 0.3563 |
| 4.6853 | 26050 | 0.0138 | - |
| 4.6942 | 26100 | 0.0001 | - |
| 4.7032 | 26150 | 0.0068 | - |
| 4.7122 | 26200 | 0.0091 | - |
| 4.7212 | 26250 | 0.0001 | - |
| 4.7302 | 26300 | 0.0152 | - |
| 4.7392 | 26350 | 0.0064 | - |
| 4.7482 | 26400 | 0.0021 | - |
| 4.7572 | 26450 | 0.0088 | - |
| 4.7662 | 26500 | 0.0001 | - |
| 4.7752 | 26550 | 0.0042 | - |
| 4.7842 | 26600 | 0.0022 | - |
| 4.7932 | 26650 | 0.0065 | - |
| 4.8022 | 26700 | 0.0039 | - |
| 4.8112 | 26750 | 0.0039 | - |
| 4.8201 | 26800 | 0.0001 | - |
| 4.8291 | 26850 | 0.0155 | - |
| 4.8381 | 26900 | 0.0021 | - |
| 4.8471 | 26950 | 0.0039 | - |
| 4.8561 | 27000 | 0.002 | 0.3555 |
| 4.8651 | 27050 | 0.0092 | - |
| 4.8741 | 27100 | 0.0001 | - |
| 4.8831 | 27150 | 0.0081 | - |
| 4.8921 | 27200 | 0.0081 | - |
| 4.9011 | 27250 | 0.0037 | - |
| 4.9101 | 27300 | 0.0104 | - |
| 4.9191 | 27350 | 0.0022 | - |
| 4.9281 | 27400 | 0.004 | - |
| 4.9371 | 27450 | 0.0076 | - |
| 4.9460 | 27500 | 0.0043 | - |
| 4.9550 | 27550 | 0.0142 | - |
| 4.9640 | 27600 | 0.0126 | - |
| 4.9730 | 27650 | 0.0038 | - |
| 4.9820 | 27700 | 0.0107 | - |
| 4.9910 | 27750 | 0.0019 | - |
| 5.0 | 27800 | 0.0104 | - |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.4
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- 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}
}
```
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