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amplyfi
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all-labels

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community

SetFit with mixedbread-ai/mxbai-embed-large-v1

This is a SetFit model that can be used for Text Classification. This SetFit model uses mixedbread-ai/mxbai-embed-large-v1 as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

  • Model Type: SetFit
  • Sentence Transformer body: mixedbread-ai/mxbai-embed-large-v1
  • Classification head: a OneVsRestClassifier instance
  • Maximum Sequence Length: 512 tokens

Model Sources

Evaluation

Metrics

Label Accuracy
all 0.6622

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("amplyfi/all-labels")
# Run inference
preds = model("Cyprus, the tech island: reflections on the Reflect Festival")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 10.0203 30

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 10
  • body_learning_rate: (2e-05, 2e-05)
  • head_learning_rate: 2e-05
  • 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
0.0009 1 0.2478 -
0.0452 50 0.2177 -
0.0903 100 0.2097 -
0.1355 150 0.1949 -
0.1807 200 0.1787 -
0.2258 250 0.1494 -
0.2710 300 0.1225 -
0.3162 350 0.1146 -
0.3613 400 0.0845 -
0.4065 450 0.0726 -
0.4517 500 0.076 -
0.4968 550 0.0579 -
0.5420 600 0.0688 -
0.5872 650 0.0533 -
0.6323 700 0.0547 -
0.6775 750 0.0492 -
0.7227 800 0.0498 -
0.7678 850 0.037 -
0.8130 900 0.0357 -
0.8582 950 0.0398 -
0.9033 1000 0.0428 -
0.9485 1050 0.0494 -
0.9937 1100 0.0334 -
1.0388 1150 0.0327 -
1.0840 1200 0.0265 -
1.1292 1250 0.0283 -
1.1743 1300 0.0347 -
1.2195 1350 0.0299 -
1.2647 1400 0.0235 -
1.3098 1450 0.0223 -
1.3550 1500 0.0254 -
1.4002 1550 0.0249 -
1.4453 1600 0.0209 -
1.4905 1650 0.0239 -
1.5357 1700 0.0229 -
1.5808 1750 0.0193 -
1.6260 1800 0.017 -
1.6712 1850 0.0205 -
1.7164 1900 0.0162 -
1.7615 1950 0.0179 -
1.8067 2000 0.0196 -
1.8519 2050 0.0138 -
1.8970 2100 0.0196 -
1.9422 2150 0.0118 -
1.9874 2200 0.0166 -
2.0325 2250 0.0104 -
2.0777 2300 0.0139 -
2.1229 2350 0.0139 -
2.1680 2400 0.0122 -
2.2132 2450 0.0128 -
2.2584 2500 0.0111 -
2.3035 2550 0.0136 -
2.3487 2600 0.0108 -
2.3939 2650 0.0112 -
2.4390 2700 0.0117 -
2.4842 2750 0.0147 -
2.5294 2800 0.0139 -
2.5745 2850 0.0137 -
2.6197 2900 0.0124 -
2.6649 2950 0.012 -
2.7100 3000 0.0118 -
2.7552 3050 0.0124 -
2.8004 3100 0.0109 -
2.8455 3150 0.01 -
2.8907 3200 0.0109 -
2.9359 3250 0.0072 -
2.9810 3300 0.0102 -
3.0262 3350 0.0102 -
3.0714 3400 0.0141 -
3.1165 3450 0.0143 -
3.1617 3500 0.0105 -
3.2069 3550 0.0132 -
3.2520 3600 0.011 -
3.2972 3650 0.0104 -
3.3424 3700 0.0104 -
3.3875 3750 0.0078 -
3.4327 3800 0.0095 -
3.4779 3850 0.0118 -
3.5230 3900 0.0076 -
3.5682 3950 0.0087 -
3.6134 4000 0.0098 -
3.6585 4050 0.0114 -
3.7037 4100 0.0086 -
3.7489 4150 0.01 -
3.7940 4200 0.0102 -
3.8392 4250 0.0077 -
3.8844 4300 0.0076 -
3.9295 4350 0.0082 -
3.9747 4400 0.0095 -
4.0199 4450 0.0055 -
4.0650 4500 0.009 -
4.1102 4550 0.0086 -
4.1554 4600 0.0086 -
4.2005 4650 0.0075 -
4.2457 4700 0.009 -
4.2909 4750 0.0068 -
4.3360 4800 0.0096 -
4.3812 4850 0.008 -
4.4264 4900 0.0075 -
4.4715 4950 0.0069 -
4.5167 5000 0.0076 -
4.5619 5050 0.0058 -
4.6070 5100 0.0077 -
4.6522 5150 0.0073 -
4.6974 5200 0.0083 -
4.7425 5250 0.0059 -
4.7877 5300 0.0066 -
4.8329 5350 0.0065 -
4.8780 5400 0.006 -
4.9232 5450 0.008 -
4.9684 5500 0.0073 -
5.0136 5550 0.01 -
5.0587 5600 0.0047 -
5.1039 5650 0.0057 -
5.1491 5700 0.0069 -
5.1942 5750 0.0055 -
5.2394 5800 0.0082 -
5.2846 5850 0.0067 -
5.3297 5900 0.0081 -
5.3749 5950 0.0079 -
5.4201 6000 0.0051 -
5.4652 6050 0.0073 -
5.5104 6100 0.007 -
5.5556 6150 0.0069 -
5.6007 6200 0.0066 -
5.6459 6250 0.0073 -
5.6911 6300 0.0063 -
5.7362 6350 0.0049 -
5.7814 6400 0.0042 -
5.8266 6450 0.0076 -
5.8717 6500 0.0077 -
5.9169 6550 0.0071 -
5.9621 6600 0.0079 -
6.0072 6650 0.0073 -
6.0524 6700 0.0069 -
6.0976 6750 0.0049 -
6.1427 6800 0.0065 -
6.1879 6850 0.0046 -
6.2331 6900 0.0063 -
6.2782 6950 0.0061 -
6.3234 7000 0.0066 -
6.3686 7050 0.0049 -
6.4137 7100 0.0048 -
6.4589 7150 0.0062 -
6.5041 7200 0.0067 -
6.5492 7250 0.0059 -
6.5944 7300 0.0078 -
6.6396 7350 0.0074 -
6.6847 7400 0.0058 -
6.7299 7450 0.007 -
6.7751 7500 0.0059 -
6.8202 7550 0.0061 -
6.8654 7600 0.0057 -
6.9106 7650 0.0062 -
6.9557 7700 0.0056 -
7.0009 7750 0.0054 -
7.0461 7800 0.0063 -
7.0912 7850 0.0066 -
7.1364 7900 0.0051 -
7.1816 7950 0.0063 -
7.2267 8000 0.0053 -
7.2719 8050 0.0045 -
7.3171 8100 0.0065 -
7.3622 8150 0.0057 -
7.4074 8200 0.0068 -
7.4526 8250 0.0058 -
7.4977 8300 0.0077 -
7.5429 8350 0.0062 -
7.5881 8400 0.0057 -
7.6332 8450 0.0047 -
7.6784 8500 0.0051 -
7.7236 8550 0.0063 -
7.7687 8600 0.0043 -
7.8139 8650 0.0041 -
7.8591 8700 0.0055 -
7.9042 8750 0.0049 -
7.9494 8800 0.0066 -
7.9946 8850 0.007 -
8.0397 8900 0.0057 -
8.0849 8950 0.0049 -
8.1301 9000 0.0043 -
8.1752 9050 0.0054 -
8.2204 9100 0.0045 -
8.2656 9150 0.0043 -
8.3107 9200 0.0054 -
8.3559 9250 0.0048 -
8.4011 9300 0.0046 -
8.4463 9350 0.0039 -
8.4914 9400 0.0073 -
8.5366 9450 0.0071 -
8.5818 9500 0.0068 -
8.6269 9550 0.0055 -
8.6721 9600 0.0062 -
8.7173 9650 0.0055 -
8.7624 9700 0.0068 -
8.8076 9750 0.0052 -
8.8528 9800 0.0049 -
8.8979 9850 0.005 -
8.9431 9900 0.0033 -
8.9883 9950 0.0064 -
9.0334 10000 0.0057 -
9.0786 10050 0.0056 -
9.1238 10100 0.0066 -
9.1689 10150 0.0046 -
9.2141 10200 0.0043 -
9.2593 10250 0.0041 -
9.3044 10300 0.0066 -
9.3496 10350 0.0046 -
9.3948 10400 0.0056 -
9.4399 10450 0.0043 -
9.4851 10500 0.0045 -
9.5303 10550 0.0048 -
9.5754 10600 0.0057 -
9.6206 10650 0.0055 -
9.6658 10700 0.0042 -
9.7109 10750 0.0063 -
9.7561 10800 0.0047 -
9.8013 10850 0.0046 -
9.8464 10900 0.0045 -
9.8916 10950 0.0047 -
9.9368 11000 0.0057 -
9.9819 11050 0.0061 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0
  • Sentence Transformers: 3.3.1
  • Transformers: 4.42.2
  • PyTorch: 2.5.1+cu124
  • Datasets: 3.1.0
  • Tokenizers: 0.19.1

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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