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+---
+library_name: setfit
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Paru:Warung Sunda murah meriah dan makanannya enak. Favorit selada air krispi
+ dan ayam bakar. Bakwan dan perkedelnya juga enak. Paru gorengnya lembut. Tak lengkap
+ kalau kebandung. Kalau tidak makan siang disini
+- text: Peanut Butter:View Hijau bikin adem... Menu Rosti nya enak2... Dessert Favorite
+ Pistachio & Peanut Butter 👍👍
+- text: varian:Restoran pancake ter unik , harus coba ada varian manis dan asin ,
+ pelayanan bagus, suasananya juga nyaman.
+- text: Tempat:Tempat bagus cozy, walau di bagian outdoor sedikit banyak serangga
+ beterbangan dan tangga yg sedikit bahaya untuk orang dewasa karena curam
+- text: pelayanan:Restoran pancake ter unik , harus coba ada varian manis dan asin
+ , pelayanan bagus, suasananya juga nyaman.
+pipeline_tag: text-classification
+inference: false
+---
+
+# SetFit Aspect Model
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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
+
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **spaCy Model:** id_core_news_trf
+- **SetFitABSA Aspect Model:** [pahri/setfit-indo-restomix-aspect](https://huggingface.co/pahri/setfit-indo-restomix-aspect)
+- **SetFitABSA Polarity Model:** [pahri/setfit-indo-restomix-polarity](https://huggingface.co/pahri/setfit-indo-restomix-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 |
+|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| aspect | - 'pilihan makanannya:instagramable tempatnya, tp pilihan makanannya terbatas, kalo untuk nongkrong ngemil sih enak tempatnya,tp kl yg mau makan berart krg bnyk variasi menunya'
- 'tempatnya:instagramable tempatnya, tp pilihan makanannya terbatas, kalo untuk nongkrong ngemil sih enak tempatnya,tp kl yg mau makan berart krg bnyk variasi menunya'
- 'Harganya:Harganya juga masih terjangkau'
|
+| no aspect | - 'tempatnya:instagramable tempatnya, tp pilihan makanannya terbatas, kalo untuk nongkrong ngemil sih enak tempatnya,tp kl yg mau makan berart krg bnyk variasi menunya'
- 'nongkrong ngemil:instagramable tempatnya, tp pilihan makanannya terbatas, kalo untuk nongkrong ngemil sih enak tempatnya,tp kl yg mau makan berart krg bnyk variasi menunya'
- 'krg:instagramable tempatnya, tp pilihan makanannya terbatas, kalo untuk nongkrong ngemil sih enak tempatnya,tp kl yg mau makan berart krg bnyk variasi menunya'
|
+
+## 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(
+ "pahri/setfit-indo-restomix-aspect",
+ "pahri/setfit-indo-restomix-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 | 4 | 31.9474 | 93 |
+
+| Label | Training Sample Count |
+|:----------|:----------------------|
+| no aspect | 900 |
+| aspect | 184 |
+
+### Training Hyperparameters
+- batch_size: (6, 6)
+- 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: True
+- 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 | 0.6186 | - |
+| 0.0004 | 50 | 0.1737 | - |
+| 0.0007 | 100 | 0.2856 | - |
+| 0.0011 | 150 | 0.4354 | - |
+| 0.0014 | 200 | 0.4087 | - |
+| 0.0018 | 250 | 0.0981 | - |
+| 0.0021 | 300 | 0.4136 | - |
+| 0.0025 | 350 | 0.2159 | - |
+| 0.0028 | 400 | 0.1172 | - |
+| 0.0032 | 450 | 0.3378 | - |
+| 0.0036 | 500 | 0.1741 | - |
+| 0.0039 | 550 | 0.3959 | - |
+| 0.0043 | 600 | 0.3034 | - |
+| 0.0046 | 650 | 0.2228 | - |
+| 0.0050 | 700 | 0.2763 | - |
+| 0.0053 | 750 | 0.2034 | - |
+| 0.0057 | 800 | 0.2469 | - |
+| 0.0060 | 850 | 0.1686 | - |
+| 0.0064 | 900 | 0.1215 | - |
+| 0.0067 | 950 | 0.2138 | - |
+| 0.0071 | 1000 | 0.341 | - |
+| 0.0075 | 1050 | 0.2645 | - |
+| 0.0078 | 1100 | 0.2137 | - |
+| 0.0082 | 1150 | 0.1974 | - |
+| 0.0085 | 1200 | 0.2382 | - |
+| 0.0089 | 1250 | 0.1328 | - |
+| 0.0092 | 1300 | 0.2413 | - |
+| 0.0096 | 1350 | 0.0887 | - |
+| 0.0099 | 1400 | 0.3279 | - |
+| 0.0103 | 1450 | 0.2244 | - |
+| 0.0107 | 1500 | 0.2066 | - |
+| 0.0110 | 1550 | 0.083 | - |
+| 0.0114 | 1600 | 0.0823 | - |
+| 0.0117 | 1650 | 0.2931 | - |
+| 0.0121 | 1700 | 0.1027 | - |
+| 0.0124 | 1750 | 0.0449 | - |
+| 0.0128 | 1800 | 0.1133 | - |
+| 0.0131 | 1850 | 0.0129 | - |
+| 0.0135 | 1900 | 0.1168 | - |
+| 0.0138 | 1950 | 0.19 | - |
+| 0.0142 | 2000 | 0.0924 | - |
+| 0.0146 | 2050 | 0.0791 | - |
+| 0.0149 | 2100 | 0.1975 | - |
+| 0.0153 | 2150 | 0.0394 | - |
+| 0.0156 | 2200 | 0.175 | - |
+| 0.0160 | 2250 | 0.1208 | - |
+| 0.0163 | 2300 | 0.3592 | - |
+| 0.0167 | 2350 | 0.2459 | - |
+| 0.0170 | 2400 | 0.0875 | - |
+| 0.0174 | 2450 | 0.1453 | - |
+| 0.0178 | 2500 | 0.0165 | - |
+| 0.0181 | 2550 | 0.0287 | - |
+| 0.0185 | 2600 | 0.1032 | - |
+| 0.0188 | 2650 | 0.0013 | - |
+| 0.0192 | 2700 | 0.2381 | - |
+| 0.0195 | 2750 | 0.2699 | - |
+| 0.0199 | 2800 | 0.0176 | - |
+| 0.0202 | 2850 | 0.0194 | - |
+| 0.0206 | 2900 | 0.2149 | - |
+| 0.0209 | 2950 | 0.0158 | - |
+| 0.0213 | 3000 | 0.0662 | - |
+| 0.0217 | 3050 | 0.2735 | - |
+| 0.0220 | 3100 | 0.033 | - |
+| 0.0224 | 3150 | 0.0389 | - |
+| 0.0227 | 3200 | 0.2099 | - |
+| 0.0231 | 3250 | 0.1385 | - |
+| 0.0234 | 3300 | 0.0437 | - |
+| 0.0238 | 3350 | 0.1676 | - |
+| 0.0241 | 3400 | 0.0083 | - |
+| 0.0245 | 3450 | 0.0864 | - |
+| 0.0249 | 3500 | 0.2832 | - |
+| 0.0252 | 3550 | 0.0256 | - |
+| 0.0256 | 3600 | 0.0055 | - |
+| 0.0259 | 3650 | 0.0016 | - |
+| 0.0263 | 3700 | 0.0735 | - |
+| 0.0266 | 3750 | 0.0354 | - |
+| 0.0270 | 3800 | 0.0013 | - |
+| 0.0273 | 3850 | 0.1766 | - |
+| 0.0277 | 3900 | 0.1091 | - |
+| 0.0280 | 3950 | 0.0615 | - |
+| 0.0284 | 4000 | 0.1663 | - |
+| 0.0288 | 4050 | 0.0057 | - |
+| 0.0291 | 4100 | 0.1452 | - |
+| 0.0295 | 4150 | 0.0007 | - |
+| 0.0298 | 4200 | 0.0407 | - |
+| 0.0302 | 4250 | 0.1546 | - |
+| 0.0305 | 4300 | 0.1313 | - |
+| 0.0309 | 4350 | 0.0005 | - |
+| 0.0312 | 4400 | 0.032 | - |
+| 0.0316 | 4450 | 0.1213 | - |
+| 0.0320 | 4500 | 0.1415 | - |
+| 0.0323 | 4550 | 0.0183 | - |
+| 0.0327 | 4600 | 0.0022 | - |
+| 0.0330 | 4650 | 0.0135 | - |
+| 0.0334 | 4700 | 0.1518 | - |
+| 0.0337 | 4750 | 0.0016 | - |
+| 0.0341 | 4800 | 0.138 | - |
+| 0.0344 | 4850 | 0.0091 | - |
+| 0.0348 | 4900 | 0.0102 | - |
+| 0.0352 | 4950 | 0.0094 | - |
+| 0.0355 | 5000 | 0.1227 | - |
+| 0.0359 | 5050 | 0.0066 | - |
+| 0.0362 | 5100 | 0.0122 | - |
+| 0.0366 | 5150 | 0.1224 | - |
+| 0.0369 | 5200 | 0.0003 | - |
+| 0.0373 | 5250 | 0.0152 | - |
+| 0.0376 | 5300 | 0.0059 | - |
+| 0.0380 | 5350 | 0.063 | - |
+| 0.0383 | 5400 | 0.1648 | - |
+| 0.0387 | 5450 | 0.1677 | - |
+| 0.0391 | 5500 | 0.0052 | - |
+| 0.0394 | 5550 | 0.0031 | - |
+| 0.0398 | 5600 | 0.0146 | - |
+| 0.0401 | 5650 | 0.018 | - |
+| 0.0405 | 5700 | 0.1606 | - |
+| 0.0408 | 5750 | 0.0016 | - |
+| 0.0412 | 5800 | 0.1635 | - |
+| 0.0415 | 5850 | 0.0017 | - |
+| 0.0419 | 5900 | 0.0173 | - |
+| 0.0423 | 5950 | 0.026 | - |
+| 0.0426 | 6000 | 0.0006 | - |
+| 0.0430 | 6050 | 0.0011 | - |
+| 0.0433 | 6100 | 0.0037 | - |
+| 0.0437 | 6150 | 0.0004 | - |
+| 0.0440 | 6200 | 0.0004 | - |
+| 0.0444 | 6250 | 0.0019 | - |
+| 0.0447 | 6300 | 0.0002 | - |
+| 0.0451 | 6350 | 0.0016 | - |
+| 0.0454 | 6400 | 0.0006 | - |
+| 0.0458 | 6450 | 0.0004 | - |
+| 0.0462 | 6500 | 0.1592 | - |
+| 0.0465 | 6550 | 0.1627 | - |
+| 0.0469 | 6600 | 0.0177 | - |
+| 0.0472 | 6650 | 0.0026 | - |
+| 0.0476 | 6700 | 0.0051 | - |
+| 0.0479 | 6750 | 0.005 | - |
+| 0.0483 | 6800 | 0.0072 | - |
+| 0.0486 | 6850 | 0.1003 | - |
+| 0.0490 | 6900 | 0.0015 | - |
+| 0.0494 | 6950 | 0.0044 | - |
+| 0.0497 | 7000 | 0.0473 | - |
+| 0.0501 | 7050 | 0.1625 | - |
+| 0.0504 | 7100 | 0.0224 | - |
+| 0.0508 | 7150 | 0.0033 | - |
+| 0.0511 | 7200 | 0.0007 | - |
+| 0.0515 | 7250 | 0.001 | - |
+| 0.0518 | 7300 | 0.0027 | - |
+| 0.0522 | 7350 | 0.0097 | - |
+| 0.0525 | 7400 | 0.005 | - |
+| 0.0529 | 7450 | 0.0001 | - |
+| 0.0533 | 7500 | 0.0448 | - |
+| 0.0536 | 7550 | 0.002 | - |
+| 0.0540 | 7600 | 0.1117 | - |
+| 0.0543 | 7650 | 0.0377 | - |
+| 0.0547 | 7700 | 0.0026 | - |
+| 0.0550 | 7750 | 0.0003 | - |
+| 0.0554 | 7800 | 0.0012 | - |
+| 0.0557 | 7850 | 0.0003 | - |
+| 0.0561 | 7900 | 0.0003 | - |
+| 0.0565 | 7950 | 0.0987 | - |
+| 0.0568 | 8000 | 0.0122 | - |
+| 0.0572 | 8050 | 0.0 | - |
+| 0.0575 | 8100 | 0.0001 | - |
+| 0.0579 | 8150 | 0.0003 | - |
+| 0.0582 | 8200 | 0.0 | - |
+| 0.0586 | 8250 | 0.0008 | - |
+| 0.0589 | 8300 | 0.2364 | - |
+| 0.0593 | 8350 | 0.0008 | - |
+| 0.0596 | 8400 | 0.0037 | - |
+| 0.0600 | 8450 | 0.0001 | - |
+| 0.0604 | 8500 | 0.0047 | - |
+| 0.0607 | 8550 | 0.0027 | - |
+| 0.0611 | 8600 | 0.0001 | - |
+| 0.0614 | 8650 | 0.0002 | - |
+| 0.0618 | 8700 | 0.0098 | - |
+| 0.0621 | 8750 | 0.1998 | - |
+| 0.0625 | 8800 | 0.165 | - |
+| 0.0628 | 8850 | 0.0004 | - |
+| 0.0632 | 8900 | 0.0691 | - |
+| 0.0636 | 8950 | 0.072 | - |
+| 0.0639 | 9000 | 0.0005 | - |
+| 0.0643 | 9050 | 0.0004 | - |
+| 0.0646 | 9100 | 0.0141 | - |
+| 0.0650 | 9150 | 0.0003 | - |
+| 0.0653 | 9200 | 0.0054 | - |
+| 0.0657 | 9250 | 0.027 | - |
+| 0.0660 | 9300 | 0.0276 | - |
+| 0.0664 | 9350 | 0.0018 | - |
+| 0.0667 | 9400 | 0.0155 | - |
+| 0.0671 | 9450 | 0.0966 | - |
+| 0.0675 | 9500 | 0.0253 | - |
+| 0.0678 | 9550 | 0.0003 | - |
+| 0.0682 | 9600 | 0.0153 | - |
+| 0.0685 | 9650 | 0.0001 | - |
+| 0.0689 | 9700 | 0.0006 | - |
+| 0.0692 | 9750 | 0.0016 | - |
+| 0.0696 | 9800 | 0.0162 | - |
+| 0.0699 | 9850 | 0.2637 | - |
+| 0.0703 | 9900 | 0.0406 | - |
+| 0.0707 | 9950 | 0.1064 | - |
+| 0.0710 | 10000 | 0.0786 | - |
+| 0.0714 | 10050 | 0.0639 | - |
+| 0.0717 | 10100 | 0.3107 | - |
+| 0.0721 | 10150 | 0.0029 | - |
+| 0.0724 | 10200 | 0.0352 | - |
+| 0.0728 | 10250 | 0.0001 | - |
+| 0.0731 | 10300 | 0.0024 | - |
+| 0.0735 | 10350 | 0.0 | - |
+| 0.0739 | 10400 | 0.0002 | - |
+| 0.0742 | 10450 | 0.0001 | - |
+| 0.0746 | 10500 | 0.253 | - |
+| 0.0749 | 10550 | 0.0003 | - |
+| 0.0753 | 10600 | 0.0007 | - |
+| 0.0756 | 10650 | 0.0037 | - |
+| 0.0760 | 10700 | 0.0004 | - |
+| 0.0763 | 10750 | 0.0 | - |
+| 0.0767 | 10800 | 0.1233 | - |
+| 0.0770 | 10850 | 0.1646 | - |
+| 0.0774 | 10900 | 0.0066 | - |
+| 0.0778 | 10950 | 0.0038 | - |
+| 0.0781 | 11000 | 0.1662 | - |
+| 0.0785 | 11050 | 0.0031 | - |
+| 0.0788 | 11100 | 0.1236 | - |
+| 0.0792 | 11150 | 0.0039 | - |
+| 0.0795 | 11200 | 0.2236 | - |
+| 0.0799 | 11250 | 0.0094 | - |
+| 0.0802 | 11300 | 0.0024 | - |
+| 0.0806 | 11350 | 0.0172 | - |
+| 0.0810 | 11400 | 0.0003 | - |
+| 0.0813 | 11450 | 0.0033 | - |
+| 0.0817 | 11500 | 0.0983 | - |
+| 0.0820 | 11550 | 0.0 | - |
+| 0.0824 | 11600 | 0.0004 | - |
+| 0.0827 | 11650 | 0.0741 | - |
+| 0.0831 | 11700 | 0.0004 | - |
+| 0.0834 | 11750 | 0.0005 | - |
+| 0.0838 | 11800 | 0.0006 | - |
+| 0.0841 | 11850 | 0.0128 | - |
+| 0.0845 | 11900 | 0.1668 | - |
+| 0.0849 | 11950 | 0.0648 | - |
+| 0.0852 | 12000 | 0.1062 | - |
+| 0.0856 | 12050 | 0.0005 | - |
+| 0.0859 | 12100 | 0.0018 | - |
+| 0.0863 | 12150 | 0.1658 | - |
+| 0.0866 | 12200 | 0.0 | - |
+| 0.0870 | 12250 | 0.0026 | - |
+| 0.0873 | 12300 | 0.1674 | - |
+| 0.0877 | 12350 | 0.0002 | - |
+| 0.0881 | 12400 | 0.0045 | - |
+| 0.0884 | 12450 | 0.0002 | - |
+| 0.0888 | 12500 | 0.1506 | - |
+| 0.0891 | 12550 | 0.0001 | - |
+| 0.0895 | 12600 | 0.1044 | - |
+| 0.0898 | 12650 | 0.0 | - |
+| 0.0902 | 12700 | 0.0425 | - |
+| 0.0905 | 12750 | 0.0 | - |
+| 0.0909 | 12800 | 0.0009 | - |
+| 0.0912 | 12850 | 0.0056 | - |
+| 0.0916 | 12900 | 0.0005 | - |
+| 0.0920 | 12950 | 0.0012 | - |
+| 0.0923 | 13000 | 0.0836 | - |
+| 0.0927 | 13050 | 0.0003 | - |
+| 0.0930 | 13100 | 0.0361 | - |
+| 0.0934 | 13150 | 0.0137 | - |
+| 0.0937 | 13200 | 0.0 | - |
+| 0.0941 | 13250 | 0.0001 | - |
+| 0.0944 | 13300 | 0.0583 | - |
+| 0.0948 | 13350 | 0.0002 | - |
+| 0.0952 | 13400 | 0.1057 | - |
+| 0.0955 | 13450 | 0.155 | - |
+| 0.0959 | 13500 | 0.1063 | - |
+| 0.0962 | 13550 | 0.0247 | - |
+| 0.0966 | 13600 | 0.0507 | - |
+| 0.0969 | 13650 | 0.0017 | - |
+| 0.0973 | 13700 | 0.0001 | - |
+| 0.0976 | 13750 | 0.0007 | - |
+| 0.0980 | 13800 | 0.0272 | - |
+| 0.0983 | 13850 | 0.0016 | - |
+| 0.0987 | 13900 | 0.0006 | - |
+| 0.0991 | 13950 | 0.0175 | - |
+| 0.0994 | 14000 | 0.0001 | - |
+| 0.0998 | 14050 | 0.0002 | - |
+| 0.1001 | 14100 | 0.002 | - |
+| 0.1005 | 14150 | 0.1704 | - |
+| 0.1008 | 14200 | 0.0 | - |
+| 0.1012 | 14250 | 0.1327 | - |
+| 0.1015 | 14300 | 0.0 | - |
+| 0.1019 | 14350 | 0.159 | - |
+| 0.1023 | 14400 | 0.0001 | - |
+| 0.1026 | 14450 | 0.0102 | - |
+| 0.1030 | 14500 | 0.0012 | - |
+| 0.1033 | 14550 | 0.0001 | - |
+| 0.1037 | 14600 | 0.0024 | - |
+| 0.1040 | 14650 | 0.0746 | - |
+| 0.1044 | 14700 | 0.0052 | - |
+| 0.1047 | 14750 | 0.011 | - |
+| 0.1051 | 14800 | 0.0077 | - |
+| 0.1055 | 14850 | 0.0089 | - |
+| 0.1058 | 14900 | 0.0018 | - |
+| 0.1062 | 14950 | 0.0 | - |
+| 0.1065 | 15000 | 0.0138 | - |
+| 0.1069 | 15050 | 0.0001 | - |
+| 0.1072 | 15100 | 0.0028 | - |
+| 0.1076 | 15150 | 0.0741 | - |
+| 0.1079 | 15200 | 0.1392 | - |
+| 0.1083 | 15250 | 0.0244 | - |
+| 0.1086 | 15300 | 0.1648 | - |
+| 0.1090 | 15350 | 0.0002 | - |
+| 0.1094 | 15400 | 0.0001 | - |
+| 0.1097 | 15450 | 0.0019 | - |
+| 0.1101 | 15500 | 0.0 | - |
+| 0.1104 | 15550 | 0.123 | - |
+| 0.1108 | 15600 | 0.0 | - |
+| 0.1111 | 15650 | 0.0001 | - |
+| 0.1115 | 15700 | 0.0001 | - |
+| 0.1118 | 15750 | 0.0 | - |
+| 0.1122 | 15800 | 0.0225 | - |
+| 0.1126 | 15850 | 0.1211 | - |
+| 0.1129 | 15900 | 0.0 | - |
+| 0.1133 | 15950 | 0.0001 | - |
+| 0.1136 | 16000 | 0.0002 | - |
+| 0.1140 | 16050 | 0.0001 | - |
+| 0.1143 | 16100 | 0.1675 | - |
+| 0.1147 | 16150 | 0.0004 | - |
+| 0.1150 | 16200 | 0.0091 | - |
+| 0.1154 | 16250 | 0.0002 | - |
+| 0.1157 | 16300 | 0.0861 | - |
+| 0.1161 | 16350 | 0.0001 | - |
+| 0.1165 | 16400 | 0.0057 | - |
+| 0.1168 | 16450 | 0.0352 | - |
+| 0.1172 | 16500 | 0.0002 | - |
+| 0.1175 | 16550 | 0.0182 | - |
+| 0.1179 | 16600 | 0.0001 | - |
+| 0.1182 | 16650 | 0.0 | - |
+| 0.1186 | 16700 | 0.0 | - |
+| 0.1189 | 16750 | 0.0002 | - |
+| 0.1193 | 16800 | 0.0091 | - |
+| 0.1197 | 16850 | 0.1558 | - |
+| 0.1200 | 16900 | 0.0 | - |
+| 0.1204 | 16950 | 0.0 | - |
+| 0.1207 | 17000 | 0.0959 | - |
+| 0.1211 | 17050 | 0.0 | - |
+| 0.1214 | 17100 | 0.0001 | - |
+| 0.1218 | 17150 | 0.1875 | - |
+| 0.1221 | 17200 | 0.0 | - |
+| 0.1225 | 17250 | 0.0 | - |
+| 0.1228 | 17300 | 0.0055 | - |
+| 0.1232 | 17350 | 0.0 | - |
+| 0.1236 | 17400 | 0.0007 | - |
+| 0.1239 | 17450 | 0.0 | - |
+| 0.1243 | 17500 | 0.0 | - |
+| 0.1246 | 17550 | 0.0 | - |
+| 0.1250 | 17600 | 0.1655 | - |
+| 0.1253 | 17650 | 0.0 | - |
+| 0.1257 | 17700 | 0.0005 | - |
+| 0.1260 | 17750 | 0.0772 | - |
+| 0.1264 | 17800 | 0.0223 | - |
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+| 0.9448 | 133050 | 0.0324 | - |
+| 0.9452 | 133100 | 0.0186 | - |
+| 0.9455 | 133150 | 0.0 | - |
+| 0.9459 | 133200 | 0.0 | - |
+| 0.9462 | 133250 | 0.095 | - |
+| 0.9466 | 133300 | 0.0 | - |
+| 0.9469 | 133350 | 0.0134 | - |
+| 0.9473 | 133400 | 0.0 | - |
+| 0.9476 | 133450 | 0.0 | - |
+| 0.9480 | 133500 | 0.0139 | - |
+| 0.9483 | 133550 | 0.0 | - |
+| 0.9487 | 133600 | 0.0251 | - |
+| 0.9491 | 133650 | 0.0 | - |
+| 0.9494 | 133700 | 0.0 | - |
+| 0.9498 | 133750 | 0.0 | - |
+| 0.9501 | 133800 | 0.0698 | - |
+| 0.9505 | 133850 | 0.0 | - |
+| 0.9508 | 133900 | 0.0132 | - |
+| 0.9512 | 133950 | 0.0 | - |
+| 0.9515 | 134000 | 0.0 | - |
+| 0.9519 | 134050 | 0.0161 | - |
+| 0.9523 | 134100 | 0.0 | - |
+| 0.9526 | 134150 | 0.0 | - |
+| 0.9530 | 134200 | 0.0134 | - |
+| 0.9533 | 134250 | 0.086 | - |
+| 0.9537 | 134300 | 0.0 | - |
+| 0.9540 | 134350 | 0.0 | - |
+| 0.9544 | 134400 | 0.0168 | - |
+| 0.9547 | 134450 | 0.0 | - |
+| 0.9551 | 134500 | 0.0 | - |
+| 0.9554 | 134550 | 0.0265 | - |
+| 0.9558 | 134600 | 0.0233 | - |
+| 0.9562 | 134650 | 0.0 | - |
+| 0.9565 | 134700 | 0.0124 | - |
+| 0.9569 | 134750 | 0.0 | - |
+| 0.9572 | 134800 | 0.0122 | - |
+| 0.9576 | 134850 | 0.0 | - |
+| 0.9579 | 134900 | 0.062 | - |
+| 0.9583 | 134950 | 0.0 | - |
+| 0.9586 | 135000 | 0.0 | - |
+| 0.9590 | 135050 | 0.0 | - |
+| 0.9594 | 135100 | 0.0 | - |
+| 0.9597 | 135150 | 0.0 | - |
+| 0.9601 | 135200 | 0.0008 | - |
+| 0.9604 | 135250 | 0.0 | - |
+| 0.9608 | 135300 | 0.0 | - |
+| 0.9611 | 135350 | 0.0867 | - |
+| 0.9615 | 135400 | 0.0804 | - |
+| 0.9618 | 135450 | 0.0 | - |
+| 0.9622 | 135500 | 0.0133 | - |
+| 0.9625 | 135550 | 0.0858 | - |
+| 0.9629 | 135600 | 0.0 | - |
+| 0.9633 | 135650 | 0.0 | - |
+| 0.9636 | 135700 | 0.0 | - |
+| 0.9640 | 135750 | 0.0807 | - |
+| 0.9643 | 135800 | 0.0 | - |
+| 0.9647 | 135850 | 0.0 | - |
+| 0.9650 | 135900 | 0.0139 | - |
+| 0.9654 | 135950 | 0.0 | - |
+| 0.9657 | 136000 | 0.0 | - |
+| 0.9661 | 136050 | 0.0047 | - |
+| 0.9665 | 136100 | 0.0 | - |
+| 0.9668 | 136150 | 0.1112 | - |
+| 0.9672 | 136200 | 0.0 | - |
+| 0.9675 | 136250 | 0.0 | - |
+| 0.9679 | 136300 | 0.0 | - |
+| 0.9682 | 136350 | 0.0138 | - |
+| 0.9686 | 136400 | 0.0 | - |
+| 0.9689 | 136450 | 0.0149 | - |
+| 0.9693 | 136500 | 0.0803 | - |
+| 0.9697 | 136550 | 0.0143 | - |
+| 0.9700 | 136600 | 0.0235 | - |
+| 0.9704 | 136650 | 0.0 | - |
+| 0.9707 | 136700 | 0.0 | - |
+| 0.9711 | 136750 | 0.0 | - |
+| 0.9714 | 136800 | 0.0143 | - |
+| 0.9718 | 136850 | 0.0138 | - |
+| 0.9721 | 136900 | 0.018 | - |
+| 0.9725 | 136950 | 0.0119 | - |
+| 0.9728 | 137000 | 0.0 | - |
+| 0.9732 | 137050 | 0.0123 | - |
+| 0.9736 | 137100 | 0.0285 | - |
+| 0.9739 | 137150 | 0.0 | - |
+| 0.9743 | 137200 | 0.0692 | - |
+| 0.9746 | 137250 | 0.0 | - |
+| 0.9750 | 137300 | 0.0 | - |
+| 0.9753 | 137350 | 0.035 | - |
+| 0.9757 | 137400 | 0.0 | - |
+| 0.9760 | 137450 | 0.0129 | - |
+| 0.9764 | 137500 | 0.0122 | - |
+| 0.9768 | 137550 | 0.0 | - |
+| 0.9771 | 137600 | 0.0128 | - |
+| 0.9775 | 137650 | 0.0 | - |
+| 0.9778 | 137700 | 0.0 | - |
+| 0.9782 | 137750 | 0.0907 | - |
+| 0.9785 | 137800 | 0.0 | - |
+| 0.9789 | 137850 | 0.0 | - |
+| 0.9792 | 137900 | 0.0 | - |
+| 0.9796 | 137950 | 0.0 | - |
+| 0.9799 | 138000 | 0.0 | - |
+| 0.9803 | 138050 | 0.0 | - |
+| 0.9807 | 138100 | 0.0 | - |
+| 0.9810 | 138150 | 0.0 | - |
+| 0.9814 | 138200 | 0.0 | - |
+| 0.9817 | 138250 | 0.0 | - |
+| 0.9821 | 138300 | 0.0048 | - |
+| 0.9824 | 138350 | 0.0 | - |
+| 0.9828 | 138400 | 0.0001 | - |
+| 0.9831 | 138450 | 0.0 | - |
+| 0.9835 | 138500 | 0.0 | - |
+| 0.9839 | 138550 | 0.0 | - |
+| 0.9842 | 138600 | 0.0 | - |
+| 0.9846 | 138650 | 0.0 | - |
+| 0.9849 | 138700 | 0.0099 | - |
+| 0.9853 | 138750 | 0.0 | - |
+| 0.9856 | 138800 | 0.011 | - |
+| 0.9860 | 138850 | 0.0116 | - |
+| 0.9863 | 138900 | 0.0 | - |
+| 0.9867 | 138950 | 0.0122 | - |
+| 0.9870 | 139000 | 0.0902 | - |
+| 0.9874 | 139050 | 0.0001 | - |
+| 0.9878 | 139100 | 0.0227 | - |
+| 0.9881 | 139150 | 0.0 | - |
+| 0.9885 | 139200 | 0.0166 | - |
+| 0.9888 | 139250 | 0.0 | - |
+| 0.9892 | 139300 | 0.0 | - |
+| 0.9895 | 139350 | 0.0828 | - |
+| 0.9899 | 139400 | 0.0 | - |
+| 0.9902 | 139450 | 0.0 | - |
+| 0.9906 | 139500 | 0.1013 | - |
+| 0.9910 | 139550 | 0.0222 | - |
+| 0.9913 | 139600 | 0.0 | - |
+| 0.9917 | 139650 | 0.0 | - |
+| 0.9920 | 139700 | 0.0 | - |
+| 0.9924 | 139750 | 0.0 | - |
+| 0.9927 | 139800 | 0.0111 | - |
+| 0.9931 | 139850 | 0.0 | - |
+| 0.9934 | 139900 | 0.0 | - |
+| 0.9938 | 139950 | 0.0 | - |
+| 0.9941 | 140000 | 0.0 | - |
+| 0.9945 | 140050 | 0.0536 | - |
+| 0.9949 | 140100 | 0.0 | - |
+| 0.9952 | 140150 | 0.0 | - |
+| 0.9956 | 140200 | 0.0 | - |
+| 0.9959 | 140250 | 0.005 | - |
+| 0.9963 | 140300 | 0.0925 | - |
+| 0.9966 | 140350 | 0.0 | - |
+| 0.9970 | 140400 | 0.0 | - |
+| 0.9973 | 140450 | 0.0115 | - |
+| 0.9977 | 140500 | 0.01 | - |
+| 0.9981 | 140550 | 0.0 | - |
+| 0.9984 | 140600 | 0.0 | - |
+| 0.9988 | 140650 | 0.0 | - |
+| 0.9991 | 140700 | 0.0 | - |
+| 0.9995 | 140750 | 0.0932 | - |
+| 0.9998 | 140800 | 0.0116 | - |
+
+### Framework Versions
+- Python: 3.10.13
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- spaCy: 3.7.4
+- Transformers: 4.36.2
+- PyTorch: 2.1.2
+- 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}
+}
+```
+
+
+
+
+
+
\ No newline at end of file