SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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: BAAI/bge-small-en-v1.5
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 43 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 |
---|---|
Latitude |
|
Categorical |
|
Day of Month |
|
Year |
|
Integer |
|
Floating Point Number |
|
Percentage |
|
Secondary Address |
|
U.S. State Abbreviation |
|
Numeric identifier |
|
Month Number |
|
Date |
|
Full Name |
|
Day of Week |
|
Timestamp |
|
URL |
|
Street Address |
|
Country ISO Code |
|
Partial timestamp |
|
Longitude |
|
Country Name |
|
Boolean |
|
Short text |
|
Slug |
|
Postal Code |
|
Structured field |
|
Alphanumeric identifier |
|
Color |
|
Month Name |
|
Currency Code |
|
Time |
|
Last Name |
|
U.S. State |
|
Street Name |
|
AM/PM |
|
Occupation |
|
Zip Code |
|
Company Name |
|
First Name |
|
Very short text |
|
License Plate |
|
URI |
|
City Name |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.6705 |
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("quantisan/bge-small-en-v1.5-93dataset")
# Run inference
preds = model("variety: Western, Eastern")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 2 | 24.0542 | 111 |
Label | Training Sample Count |
---|---|
Categorical | 8 |
Timestamp | 5 |
Date | 8 |
Integer | 8 |
Partial timestamp | 4 |
Short text | 8 |
Very short text | 3 |
AM/PM | 1 |
Boolean | 8 |
City Name | 1 |
Color | 3 |
Company Name | 1 |
Country ISO Code | 2 |
Country Name | 8 |
Currency Code | 1 |
Day of Month | 4 |
Day of Week | 4 |
First Name | 1 |
Floating Point Number | 8 |
Full Name | 8 |
Last Name | 2 |
Latitude | 4 |
License Plate | 1 |
Longitude | 4 |
Month Name | 6 |
Month Number | 4 |
Occupation | 3 |
Postal Code | 1 |
Secondary Address | 1 |
Slug | 8 |
Street Address | 3 |
Street Name | 3 |
Time | 3 |
U.S. State | 8 |
U.S. State Abbreviation | 6 |
URI | 1 |
URL | 8 |
Year | 8 |
Zip Code | 4 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (4, 4)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0002 | 1 | 0.1131 | - |
0.0100 | 50 | 0.2113 | - |
0.0200 | 100 | 0.1906 | - |
0.0301 | 150 | 0.1843 | - |
0.0401 | 200 | 0.161 | - |
0.0501 | 250 | 0.1418 | - |
0.0601 | 300 | 0.131 | - |
0.0701 | 350 | 0.1224 | - |
0.0802 | 400 | 0.114 | - |
0.0902 | 450 | 0.1039 | - |
0.1002 | 500 | 0.0825 | - |
0.1102 | 550 | 0.0838 | - |
0.1202 | 600 | 0.0745 | - |
0.1303 | 650 | 0.0705 | - |
0.1403 | 700 | 0.0586 | - |
0.1503 | 750 | 0.0552 | - |
0.1603 | 800 | 0.0567 | - |
0.1703 | 850 | 0.0553 | - |
0.1804 | 900 | 0.0456 | - |
0.1904 | 950 | 0.0443 | - |
0.2004 | 1000 | 0.0422 | - |
0.2104 | 1050 | 0.0363 | - |
0.2204 | 1100 | 0.0397 | - |
0.2305 | 1150 | 0.0361 | - |
0.2405 | 1200 | 0.0269 | - |
0.2505 | 1250 | 0.0331 | - |
0.2605 | 1300 | 0.0278 | - |
0.2705 | 1350 | 0.0297 | - |
0.2806 | 1400 | 0.022 | - |
0.2906 | 1450 | 0.0265 | - |
0.3006 | 1500 | 0.0344 | - |
0.3106 | 1550 | 0.0218 | - |
0.3206 | 1600 | 0.0275 | - |
0.3307 | 1650 | 0.0275 | - |
0.3407 | 1700 | 0.0207 | - |
0.3507 | 1750 | 0.0156 | - |
0.3607 | 1800 | 0.0246 | - |
0.3707 | 1850 | 0.0154 | - |
0.3808 | 1900 | 0.0117 | - |
0.3908 | 1950 | 0.0201 | - |
0.4008 | 2000 | 0.0153 | - |
0.4108 | 2050 | 0.018 | - |
0.4208 | 2100 | 0.017 | - |
0.4309 | 2150 | 0.011 | - |
0.4409 | 2200 | 0.0158 | - |
0.4509 | 2250 | 0.015 | - |
0.4609 | 2300 | 0.0109 | - |
0.4709 | 2350 | 0.0151 | - |
0.4810 | 2400 | 0.0085 | - |
0.4910 | 2450 | 0.0121 | - |
0.5010 | 2500 | 0.0118 | - |
0.5110 | 2550 | 0.0083 | - |
0.5210 | 2600 | 0.0094 | - |
0.5311 | 2650 | 0.0078 | - |
0.5411 | 2700 | 0.0123 | - |
0.5511 | 2750 | 0.0085 | - |
0.5611 | 2800 | 0.0046 | - |
0.5711 | 2850 | 0.0081 | - |
0.5812 | 2900 | 0.0085 | - |
0.5912 | 2950 | 0.0064 | - |
0.6012 | 3000 | 0.0113 | - |
0.6112 | 3050 | 0.0087 | - |
0.6212 | 3100 | 0.0071 | - |
0.6313 | 3150 | 0.01 | - |
0.6413 | 3200 | 0.0093 | - |
0.6513 | 3250 | 0.0056 | - |
0.6613 | 3300 | 0.007 | - |
0.6713 | 3350 | 0.0076 | - |
0.6814 | 3400 | 0.0077 | - |
0.6914 | 3450 | 0.0038 | - |
0.7014 | 3500 | 0.0051 | - |
0.7114 | 3550 | 0.0063 | - |
0.7214 | 3600 | 0.004 | - |
0.7315 | 3650 | 0.0036 | - |
0.7415 | 3700 | 0.0043 | - |
0.7515 | 3750 | 0.0086 | - |
0.7615 | 3800 | 0.0051 | - |
0.7715 | 3850 | 0.0056 | - |
0.7816 | 3900 | 0.0042 | - |
0.7916 | 3950 | 0.0062 | - |
0.8016 | 4000 | 0.0058 | - |
0.8116 | 4050 | 0.0034 | - |
0.8216 | 4100 | 0.0062 | - |
0.8317 | 4150 | 0.0091 | - |
0.8417 | 4200 | 0.0056 | - |
0.8517 | 4250 | 0.0039 | - |
0.8617 | 4300 | 0.0072 | - |
0.8717 | 4350 | 0.0051 | - |
0.8818 | 4400 | 0.0025 | - |
0.8918 | 4450 | 0.0051 | - |
0.9018 | 4500 | 0.0049 | - |
0.9118 | 4550 | 0.0024 | - |
0.9218 | 4600 | 0.0026 | - |
0.9319 | 4650 | 0.0046 | - |
0.9419 | 4700 | 0.0024 | - |
0.9519 | 4750 | 0.0026 | - |
0.9619 | 4800 | 0.0045 | - |
0.9719 | 4850 | 0.0022 | - |
0.9820 | 4900 | 0.0042 | - |
0.9920 | 4950 | 0.0067 | - |
1.0 | 4990 | - | 0.0996 |
1.0020 | 5000 | 0.0044 | - |
1.0120 | 5050 | 0.0023 | - |
1.0220 | 5100 | 0.0025 | - |
1.0321 | 5150 | 0.004 | - |
1.0421 | 5200 | 0.002 | - |
1.0521 | 5250 | 0.0042 | - |
1.0621 | 5300 | 0.0028 | - |
1.0721 | 5350 | 0.006 | - |
1.0822 | 5400 | 0.0043 | - |
1.0922 | 5450 | 0.0065 | - |
1.1022 | 5500 | 0.0042 | - |
1.1122 | 5550 | 0.004 | - |
1.1222 | 5600 | 0.0045 | - |
1.1323 | 5650 | 0.0049 | - |
1.1423 | 5700 | 0.0042 | - |
1.1523 | 5750 | 0.0044 | - |
1.1623 | 5800 | 0.002 | - |
1.1723 | 5850 | 0.0037 | - |
1.1824 | 5900 | 0.0038 | - |
1.1924 | 5950 | 0.0071 | - |
1.2024 | 6000 | 0.0044 | - |
1.2124 | 6050 | 0.0031 | - |
1.2224 | 6100 | 0.0021 | - |
1.2325 | 6150 | 0.0019 | - |
1.2425 | 6200 | 0.002 | - |
1.2525 | 6250 | 0.0059 | - |
1.2625 | 6300 | 0.002 | - |
1.2725 | 6350 | 0.0036 | - |
1.2826 | 6400 | 0.0019 | - |
1.2926 | 6450 | 0.0041 | - |
1.3026 | 6500 | 0.0042 | - |
1.3126 | 6550 | 0.0062 | - |
1.3226 | 6600 | 0.002 | - |
1.3327 | 6650 | 0.0016 | - |
1.3427 | 6700 | 0.0019 | - |
1.3527 | 6750 | 0.0055 | - |
1.3627 | 6800 | 0.0042 | - |
1.3727 | 6850 | 0.0023 | - |
1.3828 | 6900 | 0.0018 | - |
1.3928 | 6950 | 0.0041 | - |
1.4028 | 7000 | 0.008 | - |
1.4128 | 7050 | 0.0021 | - |
1.4228 | 7100 | 0.0017 | - |
1.4329 | 7150 | 0.0021 | - |
1.4429 | 7200 | 0.0017 | - |
1.4529 | 7250 | 0.0035 | - |
1.4629 | 7300 | 0.002 | - |
1.4729 | 7350 | 0.0016 | - |
1.4830 | 7400 | 0.0014 | - |
1.4930 | 7450 | 0.0041 | - |
1.5030 | 7500 | 0.0053 | - |
1.5130 | 7550 | 0.0026 | - |
1.5230 | 7600 | 0.002 | - |
1.5331 | 7650 | 0.0017 | - |
1.5431 | 7700 | 0.0017 | - |
1.5531 | 7750 | 0.0016 | - |
1.5631 | 7800 | 0.0021 | - |
1.5731 | 7850 | 0.0039 | - |
1.5832 | 7900 | 0.0034 | - |
1.5932 | 7950 | 0.0061 | - |
1.6032 | 8000 | 0.0025 | - |
1.6132 | 8050 | 0.002 | - |
1.6232 | 8100 | 0.0017 | - |
1.6333 | 8150 | 0.0016 | - |
1.6433 | 8200 | 0.0015 | - |
1.6533 | 8250 | 0.0037 | - |
1.6633 | 8300 | 0.0015 | - |
1.6733 | 8350 | 0.0035 | - |
1.6834 | 8400 | 0.0023 | - |
1.6934 | 8450 | 0.0051 | - |
1.7034 | 8500 | 0.0041 | - |
1.7134 | 8550 | 0.0018 | - |
1.7234 | 8600 | 0.0016 | - |
1.7335 | 8650 | 0.0016 | - |
1.7435 | 8700 | 0.0013 | - |
1.7535 | 8750 | 0.0022 | - |
1.7635 | 8800 | 0.0015 | - |
1.7735 | 8850 | 0.0017 | - |
1.7836 | 8900 | 0.0035 | - |
1.7936 | 8950 | 0.0013 | - |
1.8036 | 9000 | 0.0015 | - |
1.8136 | 9050 | 0.0034 | - |
1.8236 | 9100 | 0.0013 | - |
1.8337 | 9150 | 0.0037 | - |
1.8437 | 9200 | 0.0037 | - |
1.8537 | 9250 | 0.0014 | - |
1.8637 | 9300 | 0.0014 | - |
1.8737 | 9350 | 0.0013 | - |
1.8838 | 9400 | 0.0016 | - |
1.8938 | 9450 | 0.0013 | - |
1.9038 | 9500 | 0.0038 | - |
1.9138 | 9550 | 0.0013 | - |
1.9238 | 9600 | 0.0038 | - |
1.9339 | 9650 | 0.0013 | - |
1.9439 | 9700 | 0.0012 | - |
1.9539 | 9750 | 0.0016 | - |
1.9639 | 9800 | 0.0053 | - |
1.9739 | 9850 | 0.0018 | - |
1.9840 | 9900 | 0.0036 | - |
1.9940 | 9950 | 0.0014 | - |
2.0 | 9980 | - | 0.1052 |
2.0040 | 10000 | 0.0012 | - |
2.0140 | 10050 | 0.0014 | - |
2.0240 | 10100 | 0.0013 | - |
2.0341 | 10150 | 0.0014 | - |
2.0441 | 10200 | 0.0012 | - |
2.0541 | 10250 | 0.0014 | - |
2.0641 | 10300 | 0.0013 | - |
2.0741 | 10350 | 0.0012 | - |
2.0842 | 10400 | 0.0013 | - |
2.0942 | 10450 | 0.0033 | - |
2.1042 | 10500 | 0.0013 | - |
2.1142 | 10550 | 0.003 | - |
2.1242 | 10600 | 0.0036 | - |
2.1343 | 10650 | 0.0013 | - |
2.1443 | 10700 | 0.0036 | - |
2.1543 | 10750 | 0.0037 | - |
2.1643 | 10800 | 0.0024 | - |
2.1743 | 10850 | 0.0038 | - |
2.1844 | 10900 | 0.0014 | - |
2.1944 | 10950 | 0.0012 | - |
2.2044 | 11000 | 0.0035 | - |
2.2144 | 11050 | 0.0015 | - |
2.2244 | 11100 | 0.0012 | - |
2.2345 | 11150 | 0.0012 | - |
2.2445 | 11200 | 0.0011 | - |
2.2545 | 11250 | 0.0035 | - |
2.2645 | 11300 | 0.0012 | - |
2.2745 | 11350 | 0.0011 | - |
2.2846 | 11400 | 0.0011 | - |
2.2946 | 11450 | 0.0011 | - |
2.3046 | 11500 | 0.0035 | - |
2.3146 | 11550 | 0.0012 | - |
2.3246 | 11600 | 0.0011 | - |
2.3347 | 11650 | 0.0011 | - |
2.3447 | 11700 | 0.0014 | - |
2.3547 | 11750 | 0.0011 | - |
2.3647 | 11800 | 0.0011 | - |
2.3747 | 11850 | 0.0012 | - |
2.3848 | 11900 | 0.0011 | - |
2.3948 | 11950 | 0.001 | - |
2.4048 | 12000 | 0.001 | - |
2.4148 | 12050 | 0.0011 | - |
2.4248 | 12100 | 0.0011 | - |
2.4349 | 12150 | 0.0011 | - |
2.4449 | 12200 | 0.001 | - |
2.4549 | 12250 | 0.0034 | - |
2.4649 | 12300 | 0.0011 | - |
2.4749 | 12350 | 0.0013 | - |
2.4850 | 12400 | 0.0012 | - |
2.4950 | 12450 | 0.0015 | - |
2.5050 | 12500 | 0.0011 | - |
2.5150 | 12550 | 0.0034 | - |
2.5251 | 12600 | 0.001 | - |
2.5351 | 12650 | 0.0011 | - |
2.5451 | 12700 | 0.0011 | - |
2.5551 | 12750 | 0.001 | - |
2.5651 | 12800 | 0.001 | - |
2.5752 | 12850 | 0.0034 | - |
2.5852 | 12900 | 0.0033 | - |
2.5952 | 12950 | 0.0011 | - |
2.6052 | 13000 | 0.001 | - |
2.6152 | 13050 | 0.001 | - |
2.6253 | 13100 | 0.0012 | - |
2.6353 | 13150 | 0.0011 | - |
2.6453 | 13200 | 0.0033 | - |
2.6553 | 13250 | 0.0034 | - |
2.6653 | 13300 | 0.001 | - |
2.6754 | 13350 | 0.001 | - |
2.6854 | 13400 | 0.0034 | - |
2.6954 | 13450 | 0.001 | - |
2.7054 | 13500 | 0.001 | - |
2.7154 | 13550 | 0.001 | - |
2.7255 | 13600 | 0.0009 | - |
2.7355 | 13650 | 0.001 | - |
2.7455 | 13700 | 0.001 | - |
2.7555 | 13750 | 0.0009 | - |
2.7655 | 13800 | 0.001 | - |
2.7756 | 13850 | 0.0009 | - |
2.7856 | 13900 | 0.0031 | - |
2.7956 | 13950 | 0.001 | - |
2.8056 | 14000 | 0.0031 | - |
2.8156 | 14050 | 0.0033 | - |
2.8257 | 14100 | 0.001 | - |
2.8357 | 14150 | 0.0009 | - |
2.8457 | 14200 | 0.0009 | - |
2.8557 | 14250 | 0.0009 | - |
2.8657 | 14300 | 0.001 | - |
2.8758 | 14350 | 0.001 | - |
2.8858 | 14400 | 0.0033 | - |
2.8958 | 14450 | 0.001 | - |
2.9058 | 14500 | 0.001 | - |
2.9158 | 14550 | 0.001 | - |
2.9259 | 14600 | 0.0033 | - |
2.9359 | 14650 | 0.001 | - |
2.9459 | 14700 | 0.0009 | - |
2.9559 | 14750 | 0.001 | - |
2.9659 | 14800 | 0.001 | - |
2.9760 | 14850 | 0.0009 | - |
2.9860 | 14900 | 0.0009 | - |
2.9960 | 14950 | 0.0009 | - |
3.0 | 14970 | - | 0.1077 |
3.0060 | 15000 | 0.0033 | - |
3.0160 | 15050 | 0.0009 | - |
3.0261 | 15100 | 0.0009 | - |
3.0361 | 15150 | 0.0009 | - |
3.0461 | 15200 | 0.0009 | - |
3.0561 | 15250 | 0.0008 | - |
3.0661 | 15300 | 0.001 | - |
3.0762 | 15350 | 0.0009 | - |
3.0862 | 15400 | 0.0009 | - |
3.0962 | 15450 | 0.0032 | - |
3.1062 | 15500 | 0.0009 | - |
3.1162 | 15550 | 0.0009 | - |
3.1263 | 15600 | 0.0009 | - |
3.1363 | 15650 | 0.0009 | - |
3.1463 | 15700 | 0.0008 | - |
3.1563 | 15750 | 0.0009 | - |
3.1663 | 15800 | 0.0009 | - |
3.1764 | 15850 | 0.0008 | - |
3.1864 | 15900 | 0.0008 | - |
3.1964 | 15950 | 0.0009 | - |
3.2064 | 16000 | 0.0009 | - |
3.2164 | 16050 | 0.0033 | - |
3.2265 | 16100 | 0.0031 | - |
3.2365 | 16150 | 0.0008 | - |
3.2465 | 16200 | 0.0008 | - |
3.2565 | 16250 | 0.0008 | - |
3.2665 | 16300 | 0.0008 | - |
3.2766 | 16350 | 0.0008 | - |
3.2866 | 16400 | 0.0008 | - |
3.2966 | 16450 | 0.0008 | - |
3.3066 | 16500 | 0.0009 | - |
3.3166 | 16550 | 0.0008 | - |
3.3267 | 16600 | 0.0032 | - |
3.3367 | 16650 | 0.0008 | - |
3.3467 | 16700 | 0.0008 | - |
3.3567 | 16750 | 0.0009 | - |
3.3667 | 16800 | 0.0031 | - |
3.3768 | 16850 | 0.0009 | - |
3.3868 | 16900 | 0.0008 | - |
3.3968 | 16950 | 0.0009 | - |
3.4068 | 17000 | 0.0009 | - |
3.4168 | 17050 | 0.0008 | - |
3.4269 | 17100 | 0.0009 | - |
3.4369 | 17150 | 0.0031 | - |
3.4469 | 17200 | 0.0032 | - |
3.4569 | 17250 | 0.0008 | - |
3.4669 | 17300 | 0.0008 | - |
3.4770 | 17350 | 0.0008 | - |
3.4870 | 17400 | 0.0008 | - |
3.4970 | 17450 | 0.0057 | - |
3.5070 | 17500 | 0.0032 | - |
3.5170 | 17550 | 0.0009 | - |
3.5271 | 17600 | 0.0052 | - |
3.5371 | 17650 | 0.0008 | - |
3.5471 | 17700 | 0.0009 | - |
3.5571 | 17750 | 0.0008 | - |
3.5671 | 17800 | 0.0008 | - |
3.5772 | 17850 | 0.0008 | - |
3.5872 | 17900 | 0.0008 | - |
3.5972 | 17950 | 0.0009 | - |
3.6072 | 18000 | 0.0032 | - |
3.6172 | 18050 | 0.0008 | - |
3.6273 | 18100 | 0.0008 | - |
3.6373 | 18150 | 0.0008 | - |
3.6473 | 18200 | 0.0008 | - |
3.6573 | 18250 | 0.0008 | - |
3.6673 | 18300 | 0.0008 | - |
3.6774 | 18350 | 0.0008 | - |
3.6874 | 18400 | 0.0008 | - |
3.6974 | 18450 | 0.0008 | - |
3.7074 | 18500 | 0.0008 | - |
3.7174 | 18550 | 0.0007 | - |
3.7275 | 18600 | 0.0008 | - |
3.7375 | 18650 | 0.0008 | - |
3.7475 | 18700 | 0.003 | - |
3.7575 | 18750 | 0.0008 | - |
3.7675 | 18800 | 0.0008 | - |
3.7776 | 18850 | 0.0008 | - |
3.7876 | 18900 | 0.0007 | - |
3.7976 | 18950 | 0.0008 | - |
3.8076 | 19000 | 0.0007 | - |
3.8176 | 19050 | 0.0007 | - |
3.8277 | 19100 | 0.0029 | - |
3.8377 | 19150 | 0.0007 | - |
3.8477 | 19200 | 0.0008 | - |
3.8577 | 19250 | 0.0031 | - |
3.8677 | 19300 | 0.0007 | - |
3.8778 | 19350 | 0.0007 | - |
3.8878 | 19400 | 0.0008 | - |
3.8978 | 19450 | 0.0008 | - |
3.9078 | 19500 | 0.0031 | - |
3.9178 | 19550 | 0.0008 | - |
3.9279 | 19600 | 0.0008 | - |
3.9379 | 19650 | 0.0007 | - |
3.9479 | 19700 | 0.0008 | - |
3.9579 | 19750 | 0.0008 | - |
3.9679 | 19800 | 0.0008 | - |
3.9780 | 19850 | 0.0008 | - |
3.9880 | 19900 | 0.0008 | - |
3.9980 | 19950 | 0.0007 | - |
4.0 | 19960 | - | 0.1050 |
Framework Versions
- Python: 3.11.10
- SetFit: 1.1.0
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.4.1+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.0
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}
}
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Model tree for quantisan/bge-small-en-v1.5-93dataset
Base model
BAAI/bge-small-en-v1.5