metadata
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Constitutional changes prohibit selling agricultural land to foreigners.
- text: >-
Administrative procedures for outward direct investments were simplified.
Direct investment projects no longer have to be referred to the CBC for
verification of genuineness prior to the transfer of funds through
authorized dealers, unless the required amount exceeds £C 5 million a
year.
- text: >-
In addition, nonbank corporation which borrow abroad must fulfill certain
credit rating criteria.
- text: >-
The threshold above which there are no controls on direct investments by
companies not listed publicly was reduced to 50% of capital from
two-thirds. In the case of companies whose shares are listed on the stock
exchange, the threshold was increased to 50% of capital from 20%.
- text: >-
The limits on purchases of foreign securities by insurance companies were
increased to 30% from 25% of technical provisions and risk capital
reserves.
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.828125
name: Accuracy
SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 3 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 |
---|---|
neutral |
|
loosen |
|
tighten |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8281 |
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("kteoh37/setfit_sectionxi_changes_100_examples")
# Run inference
preds = model("Constitutional changes prohibit selling agricultural land to foreigners.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 6 | 38.4936 | 225 |
Label | Training Sample Count |
---|---|
loosen | 100 |
tighten | 100 |
neutral | 35 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (10, 10)
- 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: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0005 | 1 | 0.2629 | - |
0.0235 | 50 | 0.2148 | - |
0.0471 | 100 | 0.2342 | - |
0.0706 | 150 | 0.2326 | - |
0.0941 | 200 | 0.2391 | - |
0.1176 | 250 | 0.2351 | - |
0.1412 | 300 | 0.1484 | - |
0.1647 | 350 | 0.1034 | - |
0.1882 | 400 | 0.123 | - |
0.2118 | 450 | 0.064 | - |
0.2353 | 500 | 0.0697 | - |
0.2588 | 550 | 0.0367 | - |
0.2824 | 600 | 0.0695 | - |
0.3059 | 650 | 0.0044 | - |
0.3294 | 700 | 0.009 | - |
0.3529 | 750 | 0.0027 | - |
0.3765 | 800 | 0.0012 | - |
0.4 | 850 | 0.0609 | - |
0.4235 | 900 | 0.0019 | - |
0.4471 | 950 | 0.0013 | - |
0.4706 | 1000 | 0.0031 | - |
0.4941 | 1050 | 0.0004 | - |
0.5176 | 1100 | 0.0029 | - |
0.5412 | 1150 | 0.001 | - |
0.5647 | 1200 | 0.0018 | - |
0.5882 | 1250 | 0.0023 | - |
0.6118 | 1300 | 0.0003 | - |
0.6353 | 1350 | 0.0007 | - |
0.6588 | 1400 | 0.0204 | - |
0.6824 | 1450 | 0.0486 | - |
0.7059 | 1500 | 0.0056 | - |
0.7294 | 1550 | 0.0003 | - |
0.7529 | 1600 | 0.0052 | - |
0.7765 | 1650 | 0.0033 | - |
0.8 | 1700 | 0.0021 | - |
0.8235 | 1750 | 0.0003 | - |
0.8471 | 1800 | 0.0111 | - |
0.8706 | 1850 | 0.0003 | - |
0.8941 | 1900 | 0.0531 | - |
0.9176 | 1950 | 0.0075 | - |
0.9412 | 2000 | 0.0003 | - |
0.9647 | 2050 | 0.0022 | - |
0.9882 | 2100 | 0.0006 | - |
1.0 | 2125 | - | 0.1384 |
1.0118 | 2150 | 0.0001 | - |
1.0353 | 2200 | 0.0001 | - |
1.0588 | 2250 | 0.0011 | - |
1.0824 | 2300 | 0.0001 | - |
1.1059 | 2350 | 0.0002 | - |
1.1294 | 2400 | 0.0 | - |
1.1529 | 2450 | 0.0002 | - |
1.1765 | 2500 | 0.0003 | - |
1.2 | 2550 | 0.0116 | - |
1.2235 | 2600 | 0.0002 | - |
1.2471 | 2650 | 0.0002 | - |
1.2706 | 2700 | 0.0001 | - |
1.2941 | 2750 | 0.0002 | - |
1.3176 | 2800 | 0.0 | - |
1.3412 | 2850 | 0.0132 | - |
1.3647 | 2900 | 0.0265 | - |
1.3882 | 2950 | 0.0035 | - |
1.4118 | 3000 | 0.0003 | - |
1.4353 | 3050 | 0.0022 | - |
1.4588 | 3100 | 0.0013 | - |
1.4824 | 3150 | 0.0006 | - |
1.5059 | 3200 | 0.0003 | - |
1.5294 | 3250 | 0.0 | - |
1.5529 | 3300 | 0.0198 | - |
1.5765 | 3350 | 0.0001 | - |
1.6 | 3400 | 0.0 | - |
1.6235 | 3450 | 0.0001 | - |
1.6471 | 3500 | 0.0 | - |
1.6706 | 3550 | 0.0 | - |
1.6941 | 3600 | 0.0002 | - |
1.7176 | 3650 | 0.0 | - |
1.7412 | 3700 | 0.0 | - |
1.7647 | 3750 | 0.0023 | - |
1.7882 | 3800 | 0.0 | - |
1.8118 | 3850 | 0.0074 | - |
1.8353 | 3900 | 0.0004 | - |
1.8588 | 3950 | 0.0001 | - |
1.8824 | 4000 | 0.0228 | - |
1.9059 | 4050 | 0.0256 | - |
1.9294 | 4100 | 0.0316 | - |
1.9529 | 4150 | 0.0001 | - |
1.9765 | 4200 | 0.0 | - |
2.0 | 4250 | 0.0 | 0.1386 |
2.0235 | 4300 | 0.0006 | - |
2.0471 | 4350 | 0.0001 | - |
2.0706 | 4400 | 0.0072 | - |
2.0941 | 4450 | 0.0433 | - |
2.1176 | 4500 | 0.0001 | - |
2.1412 | 4550 | 0.0004 | - |
2.1647 | 4600 | 0.0 | - |
2.1882 | 4650 | 0.0024 | - |
2.2118 | 4700 | 0.0 | - |
2.2353 | 4750 | 0.0001 | - |
2.2588 | 4800 | 0.0 | - |
2.2824 | 4850 | 0.0002 | - |
2.3059 | 4900 | 0.0001 | - |
2.3294 | 4950 | 0.0 | - |
2.3529 | 5000 | 0.002 | - |
2.3765 | 5050 | 0.0303 | - |
2.4 | 5100 | 0.0799 | - |
2.4235 | 5150 | 0.0001 | - |
2.4471 | 5200 | 0.0 | - |
2.4706 | 5250 | 0.0024 | - |
2.4941 | 5300 | 0.0001 | - |
2.5176 | 5350 | 0.0138 | - |
2.5412 | 5400 | 0.0 | - |
2.5647 | 5450 | 0.0001 | - |
2.5882 | 5500 | 0.0 | - |
2.6118 | 5550 | 0.0 | - |
2.6353 | 5600 | 0.0 | - |
2.6588 | 5650 | 0.0 | - |
2.6824 | 5700 | 0.0396 | - |
2.7059 | 5750 | 0.0001 | - |
2.7294 | 5800 | 0.0 | - |
2.7529 | 5850 | 0.0002 | - |
2.7765 | 5900 | 0.0001 | - |
2.8 | 5950 | 0.0037 | - |
2.8235 | 6000 | 0.0 | - |
2.8471 | 6050 | 0.0186 | - |
2.8706 | 6100 | 0.0043 | - |
2.8941 | 6150 | 0.0315 | - |
2.9176 | 6200 | 0.0144 | - |
2.9412 | 6250 | 0.0 | - |
2.9647 | 6300 | 0.0052 | - |
2.9882 | 6350 | 0.0003 | - |
3.0 | 6375 | - | 0.1526 |
3.0118 | 6400 | 0.0 | - |
3.0353 | 6450 | 0.0002 | - |
3.0588 | 6500 | 0.0011 | - |
3.0824 | 6550 | 0.0 | - |
3.1059 | 6600 | 0.0 | - |
3.1294 | 6650 | 0.0002 | - |
3.1529 | 6700 | 0.0001 | - |
3.1765 | 6750 | 0.0002 | - |
3.2 | 6800 | 0.0191 | - |
3.2235 | 6850 | 0.0001 | - |
3.2471 | 6900 | 0.0 | - |
3.2706 | 6950 | 0.0036 | - |
3.2941 | 7000 | 0.0001 | - |
3.3176 | 7050 | 0.0197 | - |
3.3412 | 7100 | 0.0101 | - |
3.3647 | 7150 | 0.0 | - |
3.3882 | 7200 | 0.0 | - |
3.4118 | 7250 | 0.0003 | - |
3.4353 | 7300 | 0.0001 | - |
3.4588 | 7350 | 0.0 | - |
3.4824 | 7400 | 0.0001 | - |
3.5059 | 7450 | 0.0174 | - |
3.5294 | 7500 | 0.0 | - |
3.5529 | 7550 | 0.0 | - |
3.5765 | 7600 | 0.0 | - |
3.6 | 7650 | 0.0 | - |
3.6235 | 7700 | 0.0012 | - |
3.6471 | 7750 | 0.0 | - |
3.6706 | 7800 | 0.0 | - |
3.6941 | 7850 | 0.0 | - |
3.7176 | 7900 | 0.0 | - |
3.7412 | 7950 | 0.0 | - |
3.7647 | 8000 | 0.0 | - |
3.7882 | 8050 | 0.0 | - |
3.8118 | 8100 | 0.0 | - |
3.8353 | 8150 | 0.0004 | - |
3.8588 | 8200 | 0.0 | - |
3.8824 | 8250 | 0.0154 | - |
3.9059 | 8300 | 0.0201 | - |
3.9294 | 8350 | 0.0332 | - |
3.9529 | 8400 | 0.0 | - |
3.9765 | 8450 | 0.0002 | - |
4.0 | 8500 | 0.0028 | 0.1434 |
4.0235 | 8550 | 0.0001 | - |
4.0471 | 8600 | 0.0 | - |
4.0706 | 8650 | 0.0077 | - |
4.0941 | 8700 | 0.0435 | - |
4.1176 | 8750 | 0.0 | - |
4.1412 | 8800 | 0.0001 | - |
4.1647 | 8850 | 0.0 | - |
4.1882 | 8900 | 0.0024 | - |
4.2118 | 8950 | 0.0 | - |
4.2353 | 9000 | 0.0 | - |
4.2588 | 9050 | 0.0 | - |
4.2824 | 9100 | 0.0002 | - |
4.3059 | 9150 | 0.0 | - |
4.3294 | 9200 | 0.0263 | - |
4.3529 | 9250 | 0.0 | - |
4.3765 | 9300 | 0.0 | - |
4.4 | 9350 | 0.0416 | - |
4.4235 | 9400 | 0.0 | - |
4.4471 | 9450 | 0.0061 | - |
4.4706 | 9500 | 0.0121 | - |
4.4941 | 9550 | 0.0001 | - |
4.5176 | 9600 | 0.0187 | - |
4.5412 | 9650 | 0.0 | - |
4.5647 | 9700 | 0.0 | - |
4.5882 | 9750 | 0.0 | - |
4.6118 | 9800 | 0.0 | - |
4.6353 | 9850 | 0.0 | - |
4.6588 | 9900 | 0.0117 | - |
4.6824 | 9950 | 0.0367 | - |
4.7059 | 10000 | 0.006 | - |
4.7294 | 10050 | 0.0 | - |
4.7529 | 10100 | 0.0002 | - |
4.7765 | 10150 | 0.0003 | - |
4.8 | 10200 | 0.0 | - |
4.8235 | 10250 | 0.0 | - |
4.8471 | 10300 | 0.0085 | - |
4.8706 | 10350 | 0.0 | - |
4.8941 | 10400 | 0.0369 | - |
4.9176 | 10450 | 0.0091 | - |
4.9412 | 10500 | 0.0 | - |
4.9647 | 10550 | 0.0 | - |
4.9882 | 10600 | 0.0 | - |
5.0 | 10625 | - | 0.1711 |
5.0118 | 10650 | 0.0 | - |
5.0353 | 10700 | 0.0 | - |
5.0588 | 10750 | 0.0 | - |
5.0824 | 10800 | 0.0238 | - |
5.1059 | 10850 | 0.0 | - |
5.1294 | 10900 | 0.0075 | - |
5.1529 | 10950 | 0.0 | - |
5.1765 | 11000 | 0.0 | - |
5.2 | 11050 | 0.0179 | - |
5.2235 | 11100 | 0.0 | - |
5.2471 | 11150 | 0.0 | - |
5.2706 | 11200 | 0.0171 | - |
5.2941 | 11250 | 0.0002 | - |
5.3176 | 11300 | 0.0 | - |
5.3412 | 11350 | 0.0128 | - |
5.3647 | 11400 | 0.0 | - |
5.3882 | 11450 | 0.0029 | - |
5.4118 | 11500 | 0.0 | - |
5.4353 | 11550 | 0.0 | - |
5.4588 | 11600 | 0.0 | - |
5.4824 | 11650 | 0.0 | - |
5.5059 | 11700 | 0.0 | - |
5.5294 | 11750 | 0.0 | - |
5.5529 | 11800 | 0.0 | - |
5.5765 | 11850 | 0.0001 | - |
5.6 | 11900 | 0.0001 | - |
5.6235 | 11950 | 0.0001 | - |
5.6471 | 12000 | 0.0 | - |
5.6706 | 12050 | 0.0001 | - |
5.6941 | 12100 | 0.0 | - |
5.7176 | 12150 | 0.0 | - |
5.7412 | 12200 | 0.0 | - |
5.7647 | 12250 | 0.0 | - |
5.7882 | 12300 | 0.006 | - |
5.8118 | 12350 | 0.0001 | - |
5.8353 | 12400 | 0.0042 | - |
5.8588 | 12450 | 0.0001 | - |
5.8824 | 12500 | 0.0 | - |
5.9059 | 12550 | 0.017 | - |
5.9294 | 12600 | 0.0282 | - |
5.9529 | 12650 | 0.0 | - |
5.9765 | 12700 | 0.0046 | - |
6.0 | 12750 | 0.0 | 0.1451 |
6.0235 | 12800 | 0.0001 | - |
6.0471 | 12850 | 0.0002 | - |
6.0706 | 12900 | 0.0625 | - |
6.0941 | 12950 | 0.0633 | - |
6.1176 | 13000 | 0.0598 | - |
6.1412 | 13050 | 0.0001 | - |
6.1647 | 13100 | 0.0012 | - |
6.1882 | 13150 | 0.0004 | - |
6.2118 | 13200 | 0.0 | - |
6.2353 | 13250 | 0.0002 | - |
6.2588 | 13300 | 0.0 | - |
6.2824 | 13350 | 0.0608 | - |
6.3059 | 13400 | 0.0006 | - |
6.3294 | 13450 | 0.0 | - |
6.3529 | 13500 | 0.0587 | - |
6.3765 | 13550 | 0.0003 | - |
6.4 | 13600 | 0.0429 | - |
6.4235 | 13650 | 0.0 | - |
6.4471 | 13700 | 0.0 | - |
6.4706 | 13750 | 0.0001 | - |
6.4941 | 13800 | 0.0 | - |
6.5176 | 13850 | 0.0135 | - |
6.5412 | 13900 | 0.019 | - |
6.5647 | 13950 | 0.0028 | - |
6.5882 | 14000 | 0.0 | - |
6.6118 | 14050 | 0.0 | - |
6.6353 | 14100 | 0.0169 | - |
6.6588 | 14150 | 0.0167 | - |
6.6824 | 14200 | 0.0375 | - |
6.7059 | 14250 | 0.0 | - |
6.7294 | 14300 | 0.0044 | - |
6.7529 | 14350 | 0.0 | - |
6.7765 | 14400 | 0.0 | - |
6.8 | 14450 | 0.0025 | - |
6.8235 | 14500 | 0.0033 | - |
6.8471 | 14550 | 0.0145 | - |
6.8706 | 14600 | 0.0 | - |
6.8941 | 14650 | 0.0346 | - |
6.9176 | 14700 | 0.0117 | - |
6.9412 | 14750 | 0.0001 | - |
6.9647 | 14800 | 0.0 | - |
6.9882 | 14850 | 0.0 | - |
7.0 | 14875 | - | 0.1828 |
7.0118 | 14900 | 0.0 | - |
7.0353 | 14950 | 0.0 | - |
7.0588 | 15000 | 0.0031 | - |
7.0824 | 15050 | 0.0001 | - |
7.1059 | 15100 | 0.0055 | - |
7.1294 | 15150 | 0.0208 | - |
7.1529 | 15200 | 0.0 | - |
7.1765 | 15250 | 0.0 | - |
7.2 | 15300 | 0.0134 | - |
7.2235 | 15350 | 0.0 | - |
7.2471 | 15400 | 0.0222 | - |
7.2706 | 15450 | 0.0 | - |
7.2941 | 15500 | 0.0001 | - |
7.3176 | 15550 | 0.0 | - |
7.3412 | 15600 | 0.0111 | - |
7.3647 | 15650 | 0.0 | - |
7.3882 | 15700 | 0.0025 | - |
7.4118 | 15750 | 0.0 | - |
7.4353 | 15800 | 0.0 | - |
7.4588 | 15850 | 0.0 | - |
7.4824 | 15900 | 0.0 | - |
7.5059 | 15950 | 0.0 | - |
7.5294 | 16000 | 0.0 | - |
7.5529 | 16050 | 0.0 | - |
7.5765 | 16100 | 0.0 | - |
7.6 | 16150 | 0.0 | - |
7.6235 | 16200 | 0.0001 | - |
7.6471 | 16250 | 0.0203 | - |
7.6706 | 16300 | 0.0 | - |
7.6941 | 16350 | 0.0 | - |
7.7176 | 16400 | 0.0 | - |
7.7412 | 16450 | 0.0186 | - |
7.7647 | 16500 | 0.0 | - |
7.7882 | 16550 | 0.0 | - |
7.8118 | 16600 | 0.0049 | - |
7.8353 | 16650 | 0.0 | - |
7.8588 | 16700 | 0.0044 | - |
7.8824 | 16750 | 0.0266 | - |
7.9059 | 16800 | 0.015 | - |
7.9294 | 16850 | 0.0331 | - |
7.9529 | 16900 | 0.0 | - |
7.9765 | 16950 | 0.0 | - |
8.0 | 17000 | 0.0 | 0.1778 |
8.0235 | 17050 | 0.0 | - |
8.0471 | 17100 | 0.0 | - |
8.0706 | 17150 | 0.0082 | - |
8.0941 | 17200 | 0.0414 | - |
8.1176 | 17250 | 0.0 | - |
8.1412 | 17300 | 0.0 | - |
8.1647 | 17350 | 0.0025 | - |
8.1882 | 17400 | 0.0 | - |
8.2118 | 17450 | 0.0 | - |
8.2353 | 17500 | 0.0 | - |
8.2588 | 17550 | 0.0 | - |
8.2824 | 17600 | 0.0 | - |
8.3059 | 17650 | 0.0 | - |
8.3294 | 17700 | 0.0033 | - |
8.3529 | 17750 | 0.0 | - |
8.3765 | 17800 | 0.0033 | - |
8.4 | 17850 | 0.0371 | - |
8.4235 | 17900 | 0.0217 | - |
8.4471 | 17950 | 0.004 | - |
8.4706 | 18000 | 0.0 | - |
8.4941 | 18050 | 0.0 | - |
8.5176 | 18100 | 0.0179 | - |
8.5412 | 18150 | 0.0 | - |
8.5647 | 18200 | 0.0 | - |
8.5882 | 18250 | 0.0032 | - |
8.6118 | 18300 | 0.0 | - |
8.6353 | 18350 | 0.0026 | - |
8.6588 | 18400 | 0.0 | - |
8.6824 | 18450 | 0.0387 | - |
8.7059 | 18500 | 0.0 | - |
8.7294 | 18550 | 0.0 | - |
8.7529 | 18600 | 0.0204 | - |
8.7765 | 18650 | 0.0212 | - |
8.8 | 18700 | 0.0 | - |
8.8235 | 18750 | 0.0065 | - |
8.8471 | 18800 | 0.0114 | - |
8.8706 | 18850 | 0.0 | - |
8.8941 | 18900 | 0.0335 | - |
8.9176 | 18950 | 0.0147 | - |
8.9412 | 19000 | 0.0 | - |
8.9647 | 19050 | 0.0053 | - |
8.9882 | 19100 | 0.0 | - |
9.0 | 19125 | - | 0.1773 |
9.0118 | 19150 | 0.0181 | - |
9.0353 | 19200 | 0.0 | - |
9.0588 | 19250 | 0.0 | - |
9.0824 | 19300 | 0.0 | - |
9.1059 | 19350 | 0.0226 | - |
9.1294 | 19400 | 0.0 | - |
9.1529 | 19450 | 0.0 | - |
9.1765 | 19500 | 0.0 | - |
9.2 | 19550 | 0.013 | - |
9.2235 | 19600 | 0.0036 | - |
9.2471 | 19650 | 0.0 | - |
9.2706 | 19700 | 0.0 | - |
9.2941 | 19750 | 0.0 | - |
9.3176 | 19800 | 0.0 | - |
9.3412 | 19850 | 0.0537 | - |
9.3647 | 19900 | 0.0 | - |
9.3882 | 19950 | 0.0031 | - |
9.4118 | 20000 | 0.0 | - |
9.4353 | 20050 | 0.0 | - |
9.4588 | 20100 | 0.0 | - |
9.4824 | 20150 | 0.0 | - |
9.5059 | 20200 | 0.0 | - |
9.5294 | 20250 | 0.0 | - |
9.5529 | 20300 | 0.0033 | - |
9.5765 | 20350 | 0.0 | - |
9.6 | 20400 | 0.0 | - |
9.6235 | 20450 | 0.0 | - |
9.6471 | 20500 | 0.0 | - |
9.6706 | 20550 | 0.0035 | - |
9.6941 | 20600 | 0.0 | - |
9.7176 | 20650 | 0.0036 | - |
9.7412 | 20700 | 0.0035 | - |
9.7647 | 20750 | 0.0 | - |
9.7882 | 20800 | 0.0 | - |
9.8118 | 20850 | 0.0 | - |
9.8353 | 20900 | 0.0 | - |
9.8588 | 20950 | 0.0 | - |
9.8824 | 21000 | 0.0036 | - |
9.9059 | 21050 | 0.0127 | - |
9.9294 | 21100 | 0.0364 | - |
9.9529 | 21150 | 0.0 | - |
9.9765 | 21200 | 0.0 | - |
10.0 | 21250 | 0.0 | 0.1803 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.1.0
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 3.0.0
- Tokenizers: 0.15.2
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}
}