metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Just add speakers I tested this amazing-looking streaming amplifier, and
its filled with futuristic features to make your music sound epic. 3,000
of premium Wi-Fi sound
- text: >-
Alon Aboutboul Dies The Dark Knight Snowfall Actor was 60. The
actor was swimming on a beach when the scary moment happened.
- text: >-
Karnataka Train Services Disrupted By Boulder Fall Near Yedakumari
Normalcy Restored By Morning. Bengaluru Sakleshpur Train operations on
the South Western Railway route were briefly disrupted early Saturday
morning after boulders fell onto the track
- text: Ari Paparo on Google s Digital Dominance. Our guest is Ari Paparo.
- text: >-
Seen elsewhere The hill of crosses. I don t want to hear about this. He
says it again and again. 1,775 people murdered on South African farms from
1991 to 2006. I want to go away and never come back.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: intfloat/multilingual-e5-base
model-index:
- name: SetFit with intfloat/multilingual-e5-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8421052631578947
name: Accuracy
SetFit with intfloat/multilingual-e5-base
This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-base as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: intfloat/multilingual-e5-base
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 12 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| General News |
|
| Politics |
|
| Sports |
|
| Health |
|
| Lifestyle |
|
| Entertainment |
|
| Business |
|
| Technology |
|
| Religion |
|
| Crime |
|
| Science |
|
| Education |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.8421 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("Ari Paparo on Google s Digital Dominance. Our guest is Ari Paparo.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 2 | 46.3786 | 454 |
| Label | Training Sample Count |
|---|---|
| Business | 302 |
| Sports | 302 |
| Politics | 302 |
| Lifestyle | 302 |
| General News | 302 |
| Entertainment | 302 |
| Crime | 302 |
| Technology | 302 |
| Health | 302 |
| Science | 302 |
| Religion | 302 |
| Education | 302 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0002 | 1 | 0.2487 | - |
| 0.0110 | 50 | 0.3115 | - |
| 0.0221 | 100 | 0.2961 | - |
| 0.0331 | 150 | 0.2719 | - |
| 0.0442 | 200 | 0.2379 | - |
| 0.0552 | 250 | 0.222 | - |
| 0.0662 | 300 | 0.2096 | - |
| 0.0773 | 350 | 0.1889 | - |
| 0.0883 | 400 | 0.1645 | - |
| 0.0993 | 450 | 0.1465 | - |
| 0.1104 | 500 | 0.1197 | - |
| 0.1214 | 550 | 0.0931 | - |
| 0.1325 | 600 | 0.0885 | - |
| 0.1435 | 650 | 0.0695 | - |
| 0.1545 | 700 | 0.0673 | - |
| 0.1656 | 750 | 0.0648 | - |
| 0.1766 | 800 | 0.0538 | - |
| 0.1876 | 850 | 0.0485 | - |
| 0.1987 | 900 | 0.041 | - |
| 0.2097 | 950 | 0.0328 | - |
| 0.2208 | 1000 | 0.0285 | - |
| 0.2318 | 1050 | 0.0222 | - |
| 0.2428 | 1100 | 0.0192 | - |
| 0.2539 | 1150 | 0.0179 | - |
| 0.2649 | 1200 | 0.0144 | - |
| 0.2759 | 1250 | 0.0174 | - |
| 0.2870 | 1300 | 0.0119 | - |
| 0.2980 | 1350 | 0.0187 | - |
| 0.3091 | 1400 | 0.0156 | - |
| 0.3201 | 1450 | 0.0068 | - |
| 0.3311 | 1500 | 0.0068 | - |
| 0.3422 | 1550 | 0.0067 | - |
| 0.3532 | 1600 | 0.0061 | - |
| 0.3642 | 1650 | 0.0073 | - |
| 0.3753 | 1700 | 0.0047 | - |
| 0.3863 | 1750 | 0.0047 | - |
| 0.3974 | 1800 | 0.0054 | - |
| 0.4084 | 1850 | 0.0043 | - |
| 0.4194 | 1900 | 0.0022 | - |
| 0.4305 | 1950 | 0.0046 | - |
| 0.4415 | 2000 | 0.0018 | - |
| 0.4525 | 2050 | 0.0035 | - |
| 0.4636 | 2100 | 0.0007 | - |
| 0.4746 | 2150 | 0.003 | - |
| 0.4857 | 2200 | 0.0009 | - |
| 0.4967 | 2250 | 0.0042 | - |
| 0.5077 | 2300 | 0.0023 | - |
| 0.5188 | 2350 | 0.0005 | - |
| 0.5298 | 2400 | 0.0031 | - |
| 0.5408 | 2450 | 0.0016 | - |
| 0.5519 | 2500 | 0.001 | - |
| 0.5629 | 2550 | 0.0028 | - |
| 0.5740 | 2600 | 0.0011 | - |
| 0.5850 | 2650 | 0.0004 | - |
| 0.5960 | 2700 | 0.0003 | - |
| 0.6071 | 2750 | 0.0003 | - |
| 0.6181 | 2800 | 0.0017 | - |
| 0.6291 | 2850 | 0.001 | - |
| 0.6402 | 2900 | 0.0011 | - |
| 0.6512 | 2950 | 0.0004 | - |
| 0.6623 | 3000 | 0.0015 | - |
| 0.6733 | 3050 | 0.0006 | - |
| 0.6843 | 3100 | 0.0003 | - |
| 0.6954 | 3150 | 0.0002 | - |
| 0.7064 | 3200 | 0.0017 | - |
| 0.7174 | 3250 | 0.0005 | - |
| 0.7285 | 3300 | 0.0011 | - |
| 0.7395 | 3350 | 0.0006 | - |
| 0.7506 | 3400 | 0.0015 | - |
| 0.7616 | 3450 | 0.0004 | - |
| 0.7726 | 3500 | 0.0009 | - |
| 0.7837 | 3550 | 0.0016 | - |
| 0.7947 | 3600 | 0.0008 | - |
| 0.8057 | 3650 | 0.0004 | - |
| 0.8168 | 3700 | 0.0016 | - |
| 0.8278 | 3750 | 0.0003 | - |
| 0.8389 | 3800 | 0.0002 | - |
| 0.8499 | 3850 | 0.0001 | - |
| 0.8609 | 3900 | 0.0027 | - |
| 0.8720 | 3950 | 0.0029 | - |
| 0.8830 | 4000 | 0.0019 | - |
| 0.8940 | 4050 | 0.0036 | - |
| 0.9051 | 4100 | 0.0018 | - |
| 0.9161 | 4150 | 0.0018 | - |
| 0.9272 | 4200 | 0.0021 | - |
| 0.9382 | 4250 | 0.0003 | - |
| 0.9492 | 4300 | 0.0002 | - |
| 0.9603 | 4350 | 0.0001 | - |
| 0.9713 | 4400 | 0.0002 | - |
| 0.9823 | 4450 | 0.0016 | - |
| 0.9934 | 4500 | 0.0003 | - |
| 1.0044 | 4550 | 0.0015 | - |
| 1.0155 | 4600 | 0.0008 | - |
| 1.0265 | 4650 | 0.0002 | - |
| 1.0375 | 4700 | 0.0001 | - |
| 1.0486 | 4750 | 0.0007 | - |
| 1.0596 | 4800 | 0.0007 | - |
| 1.0706 | 4850 | 0.0001 | - |
| 1.0817 | 4900 | 0.0001 | - |
| 1.0927 | 4950 | 0.0001 | - |
| 1.1038 | 5000 | 0.0001 | - |
| 1.1148 | 5050 | 0.0001 | - |
| 1.1258 | 5100 | 0.0001 | - |
| 1.1369 | 5150 | 0.0001 | - |
| 1.1479 | 5200 | 0.0001 | - |
| 1.1589 | 5250 | 0.0001 | - |
| 1.1700 | 5300 | 0.0001 | - |
| 1.1810 | 5350 | 0.0001 | - |
| 1.1921 | 5400 | 0.0015 | - |
| 1.2031 | 5450 | 0.0045 | - |
| 1.2141 | 5500 | 0.0037 | - |
| 1.2252 | 5550 | 0.005 | - |
| 1.2362 | 5600 | 0.0006 | - |
| 1.2472 | 5650 | 0.0001 | - |
| 1.2583 | 5700 | 0.001 | - |
| 1.2693 | 5750 | 0.0001 | - |
| 1.2804 | 5800 | 0.0001 | - |
| 1.2914 | 5850 | 0.0022 | - |
| 1.3024 | 5900 | 0.0003 | - |
| 1.3135 | 5950 | 0.0016 | - |
| 1.3245 | 6000 | 0.0003 | - |
| 1.3355 | 6050 | 0.0001 | - |
| 1.3466 | 6100 | 0.0001 | - |
| 1.3576 | 6150 | 0.0001 | - |
| 1.3687 | 6200 | 0.0001 | - |
| 1.3797 | 6250 | 0.0002 | - |
| 1.3907 | 6300 | 0.0001 | - |
| 1.4018 | 6350 | 0.0001 | - |
| 1.4128 | 6400 | 0.0011 | - |
| 1.4238 | 6450 | 0.0003 | - |
| 1.4349 | 6500 | 0.0004 | - |
| 1.4459 | 6550 | 0.0001 | - |
| 1.4570 | 6600 | 0.0021 | - |
| 1.4680 | 6650 | 0.0013 | - |
| 1.4790 | 6700 | 0.0038 | - |
| 1.4901 | 6750 | 0.0002 | - |
| 1.5011 | 6800 | 0.0007 | - |
| 1.5121 | 6850 | 0.0001 | - |
| 1.5232 | 6900 | 0.0002 | - |
| 1.5342 | 6950 | 0.0014 | - |
| 1.5453 | 7000 | 0.0003 | - |
| 1.5563 | 7050 | 0.0001 | - |
| 1.5673 | 7100 | 0.0001 | - |
| 1.5784 | 7150 | 0.0001 | - |
| 1.5894 | 7200 | 0.0011 | - |
| 1.6004 | 7250 | 0.0001 | - |
| 1.6115 | 7300 | 0.0001 | - |
| 1.6225 | 7350 | 0.0001 | - |
| 1.6336 | 7400 | 0.0001 | - |
| 1.6446 | 7450 | 0.0 | - |
| 1.6556 | 7500 | 0.0 | - |
| 1.6667 | 7550 | 0.0 | - |
| 1.6777 | 7600 | 0.0 | - |
| 1.6887 | 7650 | 0.0 | - |
| 1.6998 | 7700 | 0.0 | - |
| 1.7108 | 7750 | 0.0 | - |
| 1.7219 | 7800 | 0.0 | - |
| 1.7329 | 7850 | 0.0 | - |
| 1.7439 | 7900 | 0.0001 | - |
| 1.7550 | 7950 | 0.0 | - |
| 1.7660 | 8000 | 0.0 | - |
| 1.7770 | 8050 | 0.0 | - |
| 1.7881 | 8100 | 0.0 | - |
| 1.7991 | 8150 | 0.0 | - |
| 1.8102 | 8200 | 0.0 | - |
| 1.8212 | 8250 | 0.0 | - |
| 1.8322 | 8300 | 0.0 | - |
| 1.8433 | 8350 | 0.0001 | - |
| 1.8543 | 8400 | 0.0018 | - |
| 1.8653 | 8450 | 0.0017 | - |
| 1.8764 | 8500 | 0.0001 | - |
| 1.8874 | 8550 | 0.0001 | - |
| 1.8985 | 8600 | 0.0001 | - |
| 1.9095 | 8650 | 0.0 | - |
| 1.9205 | 8700 | 0.0 | - |
| 1.9316 | 8750 | 0.0 | - |
| 1.9426 | 8800 | 0.0001 | - |
| 1.9536 | 8850 | 0.0001 | - |
| 1.9647 | 8900 | 0.0007 | - |
| 1.9757 | 8950 | 0.0015 | - |
| 1.9868 | 9000 | 0.0012 | - |
| 1.9978 | 9050 | 0.0015 | - |
| 2.0088 | 9100 | 0.0017 | - |
| 2.0199 | 9150 | 0.0021 | - |
| 2.0309 | 9200 | 0.0008 | - |
| 2.0419 | 9250 | 0.0033 | - |
| 2.0530 | 9300 | 0.0019 | - |
| 2.0640 | 9350 | 0.0002 | - |
| 2.0751 | 9400 | 0.0001 | - |
| 2.0861 | 9450 | 0.0 | - |
| 2.0971 | 9500 | 0.0 | - |
| 2.1082 | 9550 | 0.0 | - |
| 2.1192 | 9600 | 0.0 | - |
| 2.1302 | 9650 | 0.0 | - |
| 2.1413 | 9700 | 0.0 | - |
| 2.1523 | 9750 | 0.0 | - |
| 2.1634 | 9800 | 0.0001 | - |
| 2.1744 | 9850 | 0.0 | - |
| 2.1854 | 9900 | 0.0008 | - |
| 2.1965 | 9950 | 0.0143 | - |
| 2.2075 | 10000 | 0.0043 | - |
| 2.2185 | 10050 | 0.0067 | - |
| 2.2296 | 10100 | 0.0043 | - |
| 2.2406 | 10150 | 0.0017 | - |
| 2.2517 | 10200 | 0.0002 | - |
| 2.2627 | 10250 | 0.0022 | - |
| 2.2737 | 10300 | 0.0024 | - |
| 2.2848 | 10350 | 0.0004 | - |
| 2.2958 | 10400 | 0.0001 | - |
| 2.3068 | 10450 | 0.002 | - |
| 2.3179 | 10500 | 0.0001 | - |
| 2.3289 | 10550 | 0.001 | - |
| 2.3400 | 10600 | 0.0002 | - |
| 2.3510 | 10650 | 0.0002 | - |
| 2.3620 | 10700 | 0.0001 | - |
| 2.3731 | 10750 | 0.0 | - |
| 2.3841 | 10800 | 0.0016 | - |
| 2.3951 | 10850 | 0.0002 | - |
| 2.4062 | 10900 | 0.0012 | - |
| 2.4172 | 10950 | 0.0 | - |
| 2.4283 | 11000 | 0.0001 | - |
| 2.4393 | 11050 | 0.0002 | - |
| 2.4503 | 11100 | 0.0001 | - |
| 2.4614 | 11150 | 0.0001 | - |
| 2.4724 | 11200 | 0.0 | - |
| 2.4834 | 11250 | 0.0 | - |
| 2.4945 | 11300 | 0.0001 | - |
| 2.5055 | 11350 | 0.0 | - |
| 2.5166 | 11400 | 0.0 | - |
| 2.5276 | 11450 | 0.0 | - |
| 2.5386 | 11500 | 0.0 | - |
| 2.5497 | 11550 | 0.0 | - |
| 2.5607 | 11600 | 0.0 | - |
| 2.5717 | 11650 | 0.0 | - |
| 2.5828 | 11700 | 0.0 | - |
| 2.5938 | 11750 | 0.0 | - |
| 2.6049 | 11800 | 0.0 | - |
| 2.6159 | 11850 | 0.0 | - |
| 2.6269 | 11900 | 0.0 | - |
| 2.6380 | 11950 | 0.0 | - |
| 2.6490 | 12000 | 0.0 | - |
| 2.6600 | 12050 | 0.0 | - |
| 2.6711 | 12100 | 0.0 | - |
| 2.6821 | 12150 | 0.0 | - |
| 2.6932 | 12200 | 0.0 | - |
| 2.7042 | 12250 | 0.0 | - |
| 2.7152 | 12300 | 0.0 | - |
| 2.7263 | 12350 | 0.0 | - |
| 2.7373 | 12400 | 0.0 | - |
| 2.7483 | 12450 | 0.0 | - |
| 2.7594 | 12500 | 0.0 | - |
| 2.7704 | 12550 | 0.0 | - |
| 2.7815 | 12600 | 0.0 | - |
| 2.7925 | 12650 | 0.0 | - |
| 2.8035 | 12700 | 0.0 | - |
| 2.8146 | 12750 | 0.0 | - |
| 2.8256 | 12800 | 0.0 | - |
| 2.8366 | 12850 | 0.0 | - |
| 2.8477 | 12900 | 0.0 | - |
| 2.8587 | 12950 | 0.0 | - |
| 2.8698 | 13000 | 0.0 | - |
| 2.8808 | 13050 | 0.0 | - |
| 2.8918 | 13100 | 0.0 | - |
| 2.9029 | 13150 | 0.0 | - |
| 2.9139 | 13200 | 0.0 | - |
| 2.9249 | 13250 | 0.0 | - |
| 2.9360 | 13300 | 0.0 | - |
| 2.9470 | 13350 | 0.0 | - |
| 2.9581 | 13400 | 0.0 | - |
| 2.9691 | 13450 | 0.0 | - |
| 2.9801 | 13500 | 0.0 | - |
| 2.9912 | 13550 | 0.0 | - |
| 3.0022 | 13600 | 0.0 | - |
| 3.0132 | 13650 | 0.0 | - |
| 3.0243 | 13700 | 0.0 | - |
| 3.0353 | 13750 | 0.0 | - |
| 3.0464 | 13800 | 0.0 | - |
| 3.0574 | 13850 | 0.0 | - |
| 3.0684 | 13900 | 0.0 | - |
| 3.0795 | 13950 | 0.0 | - |
| 3.0905 | 14000 | 0.0 | - |
| 3.1015 | 14050 | 0.0 | - |
| 3.1126 | 14100 | 0.0 | - |
| 3.1236 | 14150 | 0.0 | - |
| 3.1347 | 14200 | 0.0 | - |
| 3.1457 | 14250 | 0.0 | - |
| 3.1567 | 14300 | 0.0 | - |
| 3.1678 | 14350 | 0.0 | - |
| 3.1788 | 14400 | 0.0 | - |
| 3.1898 | 14450 | 0.0 | - |
| 3.2009 | 14500 | 0.0 | - |
| 3.2119 | 14550 | 0.0 | - |
| 3.2230 | 14600 | 0.0 | - |
| 3.2340 | 14650 | 0.0 | - |
| 3.2450 | 14700 | 0.0 | - |
| 3.2561 | 14750 | 0.0 | - |
| 3.2671 | 14800 | 0.0 | - |
| 3.2781 | 14850 | 0.0 | - |
| 3.2892 | 14900 | 0.0 | - |
| 3.3002 | 14950 | 0.0 | - |
| 3.3113 | 15000 | 0.0 | - |
| 3.3223 | 15050 | 0.0 | - |
| 3.3333 | 15100 | 0.0 | - |
| 3.3444 | 15150 | 0.0 | - |
| 3.3554 | 15200 | 0.0 | - |
| 3.3664 | 15250 | 0.0 | - |
| 3.3775 | 15300 | 0.0 | - |
| 3.3885 | 15350 | 0.0 | - |
| 3.3996 | 15400 | 0.0 | - |
| 3.4106 | 15450 | 0.0 | - |
| 3.4216 | 15500 | 0.0 | - |
| 3.4327 | 15550 | 0.0 | - |
| 3.4437 | 15600 | 0.0 | - |
| 3.4547 | 15650 | 0.0 | - |
| 3.4658 | 15700 | 0.0 | - |
| 3.4768 | 15750 | 0.0 | - |
| 3.4879 | 15800 | 0.0 | - |
| 3.4989 | 15850 | 0.0 | - |
| 3.5099 | 15900 | 0.0 | - |
| 3.5210 | 15950 | 0.0 | - |
| 3.5320 | 16000 | 0.0 | - |
| 3.5430 | 16050 | 0.0 | - |
| 3.5541 | 16100 | 0.0 | - |
| 3.5651 | 16150 | 0.0 | - |
| 3.5762 | 16200 | 0.0 | - |
| 3.5872 | 16250 | 0.0 | - |
| 3.5982 | 16300 | 0.0 | - |
| 3.6093 | 16350 | 0.0 | - |
| 3.6203 | 16400 | 0.0 | - |
| 3.6313 | 16450 | 0.0 | - |
| 3.6424 | 16500 | 0.0 | - |
| 3.6534 | 16550 | 0.0 | - |
| 3.6645 | 16600 | 0.0 | - |
| 3.6755 | 16650 | 0.0 | - |
| 3.6865 | 16700 | 0.0 | - |
| 3.6976 | 16750 | 0.0 | - |
| 3.7086 | 16800 | 0.0 | - |
| 3.7196 | 16850 | 0.0 | - |
| 3.7307 | 16900 | 0.0 | - |
| 3.7417 | 16950 | 0.0 | - |
| 3.7528 | 17000 | 0.0 | - |
| 3.7638 | 17050 | 0.0 | - |
| 3.7748 | 17100 | 0.0 | - |
| 3.7859 | 17150 | 0.0 | - |
| 3.7969 | 17200 | 0.0 | - |
| 3.8079 | 17250 | 0.0 | - |
| 3.8190 | 17300 | 0.0 | - |
| 3.8300 | 17350 | 0.0 | - |
| 3.8411 | 17400 | 0.0 | - |
| 3.8521 | 17450 | 0.0 | - |
| 3.8631 | 17500 | 0.0 | - |
| 3.8742 | 17550 | 0.0 | - |
| 3.8852 | 17600 | 0.0 | - |
| 3.8962 | 17650 | 0.0 | - |
| 3.9073 | 17700 | 0.0 | - |
| 3.9183 | 17750 | 0.0 | - |
| 3.9294 | 17800 | 0.0 | - |
| 3.9404 | 17850 | 0.0 | - |
| 3.9514 | 17900 | 0.0 | - |
| 3.9625 | 17950 | 0.0 | - |
| 3.9735 | 18000 | 0.0 | - |
| 3.9845 | 18050 | 0.0 | - |
| 3.9956 | 18100 | 0.0 | - |
| 4.0066 | 18150 | 0.0 | - |
| 4.0177 | 18200 | 0.0 | - |
| 4.0287 | 18250 | 0.0 | - |
| 4.0397 | 18300 | 0.0 | - |
| 4.0508 | 18350 | 0.0 | - |
| 4.0618 | 18400 | 0.0 | - |
| 4.0728 | 18450 | 0.0 | - |
| 4.0839 | 18500 | 0.0 | - |
| 4.0949 | 18550 | 0.0 | - |
| 4.1060 | 18600 | 0.0 | - |
| 4.1170 | 18650 | 0.0 | - |
| 4.1280 | 18700 | 0.0 | - |
| 4.1391 | 18750 | 0.0 | - |
| 4.1501 | 18800 | 0.0 | - |
| 4.1611 | 18850 | 0.0 | - |
| 4.1722 | 18900 | 0.0 | - |
| 4.1832 | 18950 | 0.0 | - |
| 4.1943 | 19000 | 0.0 | - |
| 4.2053 | 19050 | 0.0 | - |
| 4.2163 | 19100 | 0.0 | - |
| 4.2274 | 19150 | 0.0 | - |
| 4.2384 | 19200 | 0.0 | - |
| 4.2494 | 19250 | 0.0 | - |
| 4.2605 | 19300 | 0.0 | - |
| 4.2715 | 19350 | 0.0 | - |
| 4.2826 | 19400 | 0.0 | - |
| 4.2936 | 19450 | 0.0 | - |
| 4.3046 | 19500 | 0.0 | - |
| 4.3157 | 19550 | 0.0 | - |
| 4.3267 | 19600 | 0.0 | - |
| 4.3377 | 19650 | 0.0 | - |
| 4.3488 | 19700 | 0.0 | - |
| 4.3598 | 19750 | 0.0 | - |
| 4.3709 | 19800 | 0.0 | - |
| 4.3819 | 19850 | 0.0 | - |
| 4.3929 | 19900 | 0.0 | - |
| 4.4040 | 19950 | 0.0 | - |
| 4.4150 | 20000 | 0.0 | - |
| 4.4260 | 20050 | 0.0 | - |
| 4.4371 | 20100 | 0.0 | - |
| 4.4481 | 20150 | 0.0 | - |
| 4.4592 | 20200 | 0.0 | - |
| 4.4702 | 20250 | 0.0 | - |
| 4.4812 | 20300 | 0.0 | - |
| 4.4923 | 20350 | 0.0 | - |
| 4.5033 | 20400 | 0.0 | - |
| 4.5143 | 20450 | 0.0 | - |
| 4.5254 | 20500 | 0.0 | - |
| 4.5364 | 20550 | 0.0 | - |
| 4.5475 | 20600 | 0.0 | - |
| 4.5585 | 20650 | 0.0 | - |
| 4.5695 | 20700 | 0.0 | - |
| 4.5806 | 20750 | 0.0 | - |
| 4.5916 | 20800 | 0.0 | - |
| 4.6026 | 20850 | 0.0 | - |
| 4.6137 | 20900 | 0.0 | - |
| 4.6247 | 20950 | 0.0 | - |
| 4.6358 | 21000 | 0.0 | - |
| 4.6468 | 21050 | 0.0 | - |
| 4.6578 | 21100 | 0.0 | - |
| 4.6689 | 21150 | 0.0 | - |
| 4.6799 | 21200 | 0.0 | - |
| 4.6909 | 21250 | 0.0 | - |
| 4.7020 | 21300 | 0.0 | - |
| 4.7130 | 21350 | 0.0 | - |
| 4.7241 | 21400 | 0.0 | - |
| 4.7351 | 21450 | 0.0 | - |
| 4.7461 | 21500 | 0.0 | - |
| 4.7572 | 21550 | 0.0 | - |
| 4.7682 | 21600 | 0.0 | - |
| 4.7792 | 21650 | 0.0 | - |
| 4.7903 | 21700 | 0.0 | - |
| 4.8013 | 21750 | 0.0 | - |
| 4.8124 | 21800 | 0.0 | - |
| 4.8234 | 21850 | 0.0 | - |
| 4.8344 | 21900 | 0.0 | - |
| 4.8455 | 21950 | 0.0 | - |
| 4.8565 | 22000 | 0.0 | - |
| 4.8675 | 22050 | 0.0 | - |
| 4.8786 | 22100 | 0.0 | - |
| 4.8896 | 22150 | 0.0 | - |
| 4.9007 | 22200 | 0.0 | - |
| 4.9117 | 22250 | 0.0 | - |
| 4.9227 | 22300 | 0.0 | - |
| 4.9338 | 22350 | 0.0 | - |
| 4.9448 | 22400 | 0.0 | - |
| 4.9558 | 22450 | 0.0 | - |
| 4.9669 | 22500 | 0.0 | - |
| 4.9779 | 22550 | 0.0 | - |
| 4.9890 | 22600 | 0.0 | - |
| 5.0 | 22650 | 0.0 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.3
- Sentence Transformers: 5.1.0
- Transformers: 4.55.2
- PyTorch: 2.7.1+cu118
- Datasets: 4.0.0
- Tokenizers: 0.21.4
Citation
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}
}