--- base_model: BAAI/bge-base-en-v1.5 library_name: setfit metrics: - f1 - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Discussion on recent report publication - text: Growth - text: The roundtable was arranged in order to provide an overview of the work of Alliance members and promote international development policy positions to the Scottish Conservatives. During the meeting we presented the work of SCIAF and its campaign for a world leading climate change response. In particular SCIAF explained how climate change is already affecting some of the poorest communities in the world and is therefore a central concern for international development. We argued that Scotland needs to do what it can to mitigate climate change. - text: To introduce Energy UK discuss the energy industries contribution to tackling climate change and discuss stage 1 of theClimate Change (Emissions Reduction Targets) (Scotland) Bill. Also discussed the Scottish Government's ambition on electric vehicles and the role of the energy industry in a successful roll out. - text: To discuss our key asks on the Climate Change (Emissions Reduction Targets) (Scotland) Bill in advance of Stage 2 including support for amendments on regional land use partnerships and land use strategy as means to deliver climate mitigation for land. inference: True model-index: - name: SetFit with BAAI/bge-base-en-v1.5 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: f1 value: 0.9667149059334297 name: F1 - type: accuracy value: 0.9420654911838791 name: Accuracy --- # SetFit with BAAI/bge-base-en-v1.5 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. undefined = Health 1 = Housing 2 = Defence 3 = Climate ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 512 tokens ### 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) ## Evaluation ### Metrics | Label | F1 | Accuracy | |:--------|:-------|:---------| | **all** | 0.9667 | 0.9421 | ## 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 SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("twright8/setfit_lobbying_classifier") # Run inference preds = model("Growth") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 39.4538 | 282 | ### Training Hyperparameters - batch_size: (16, 2) - num_epochs: (4, 9) - max_steps: -1 - sampling_strategy: undersampling - body_learning_rate: (1.0797496673911536e-05, 3.457046714445997e-05) - head_learning_rate: 0.0004470582121407239 - loss: CoSENTLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: True - 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 | |:-------:|:--------:|:-------------:|:---------------:| | 0.0002 | 1 | 2.097 | - | | 0.0077 | 50 | 8.5514 | - | | 0.0155 | 100 | 3.5635 | - | | 0.0232 | 150 | 2.9266 | - | | 0.0310 | 200 | 2.1173 | - | | 0.0387 | 250 | 3.1002 | - | | 0.0465 | 300 | 3.6942 | - | | 0.0542 | 350 | 3.4905 | - | | 0.0620 | 400 | 4.0804 | - | | 0.0697 | 450 | 1.6071 | - | | 0.0774 | 500 | 2.3018 | - | | 0.0852 | 550 | 2.3876 | - | | 0.0929 | 600 | 0.2511 | - | | 0.1007 | 650 | 0.2435 | - | | 0.1084 | 700 | 2.2596 | - | | 0.1162 | 750 | 1.121 | - | | 0.1239 | 800 | 0.0907 | - | | 0.1317 | 850 | 0.2172 | - | | 0.1394 | 900 | 3.06 | - | | 0.1471 | 950 | 0.0074 | - | | 0.1549 | 1000 | 0.457 | - | | 0.1626 | 1050 | 0.0575 | - | | 0.1704 | 1100 | 0.0002 | - | | 0.1781 | 1150 | 0.0003 | - | | 0.1859 | 1200 | 0.0047 | - | | 0.1936 | 1250 | 0.0004 | - | | 0.2014 | 1300 | 0.0006 | - | | 0.2091 | 1350 | 0.0027 | - | | 0.2169 | 1400 | 0.0004 | - | | 0.2246 | 1450 | 0.0009 | - | | 0.2323 | 1500 | 0.0006 | - | | 0.2401 | 1550 | 0.0003 | - | | 0.2478 | 1600 | 0.0077 | - | | 0.2556 | 1650 | 0.0004 | - | | 0.2633 | 1700 | 0.0003 | - | | 0.2711 | 1750 | 0.0005 | - | | 0.2788 | 1800 | 0.0004 | - | | 0.2866 | 1850 | 0.0007 | - | | 0.2943 | 1900 | 0.0009 | - | | 0.3020 | 1950 | 0.0062 | - | | 0.3098 | 2000 | 0.0003 | - | | 0.3175 | 2050 | 0.0001 | - | | 0.3253 | 2100 | 0.0685 | - | | 0.3330 | 2150 | 0.0008 | - | | 0.3408 | 2200 | 0.0 | - | | 0.3485 | 2250 | 0.0004 | - | | 0.3563 | 2300 | 0.0004 | - | | 0.3640 | 2350 | 0.0002 | - | | 0.3717 | 2400 | 0.0001 | - | | 0.3795 | 2450 | 0.0004 | - | | 0.3872 | 2500 | 0.0004 | - | | 0.3950 | 2550 | 0.0001 | - | | 0.4027 | 2600 | 0.0001 | - | | 0.4105 | 2650 | 0.0001 | - | | 0.4182 | 2700 | 0.0005 | - | | 0.4260 | 2750 | 0.0002 | - | | 0.4337 | 2800 | 0.0001 | - | | 0.4414 | 2850 | 0.0003 | - | | 0.4492 | 2900 | 0.0005 | - | | 0.4569 | 2950 | 0.0014 | - | | 0.4647 | 3000 | 0.0001 | - | | 0.4724 | 3050 | 0.0001 | - | | 0.4802 | 3100 | 0.0002 | - | | 0.4879 | 3150 | 0.0 | - | | 0.4957 | 3200 | 0.0006 | - | | 0.5034 | 3250 | 0.0 | - | | 0.5112 | 3300 | 0.0 | - | | 0.5189 | 3350 | 0.0002 | - | | 0.5266 | 3400 | 0.0001 | - | | 0.5344 | 3450 | 0.0006 | - | | 0.5421 | 3500 | 0.0002 | - | | 0.5499 | 3550 | 0.0001 | - | | 0.5576 | 3600 | 0.0001 | - | | 0.5654 | 3650 | 0.0001 | - | | 0.5731 | 3700 | 0.0 | - | | 0.5809 | 3750 | 0.0002 | - | | 0.5886 | 3800 | 0.0 | - | | 0.5963 | 3850 | 0.0044 | - | | 0.6041 | 3900 | 0.0002 | - | | 0.6118 | 3950 | 0.0001 | - | | 0.6196 | 4000 | 0.0003 | - | | 0.6273 | 4050 | 0.0005 | - | | 0.6351 | 4100 | 0.0002 | - | | 0.6428 | 4150 | 0.0 | - | | 0.6506 | 4200 | 0.0003 | - | | 0.6583 | 4250 | 0.0 | - | | 0.6660 | 4300 | 0.0001 | - | | 0.6738 | 4350 | 0.0 | - | | 0.6815 | 4400 | 0.0008 | - | | 0.6893 | 4450 | 0.0 | - | | 0.6970 | 4500 | 0.0004 | - | | 0.7048 | 4550 | 0.0001 | - | | 0.7125 | 4600 | 0.0 | - | | 0.7203 | 4650 | 0.0 | - | | 0.7280 | 4700 | 0.0 | - | | 0.7357 | 4750 | 0.0001 | - | | 0.7435 | 4800 | 0.0001 | - | | 0.7512 | 4850 | 0.001 | - | | 0.7590 | 4900 | 0.0001 | - | | 0.7667 | 4950 | 0.0 | - | | 0.7745 | 5000 | 0.0001 | - | | 0.7822 | 5050 | 0.0 | - | | 0.7900 | 5100 | 0.0018 | - | | 0.7977 | 5150 | 0.0001 | - | | 0.8055 | 5200 | 0.0 | - | | 0.8132 | 5250 | 0.0003 | - | | 0.8209 | 5300 | 0.0003 | - | | 0.8287 | 5350 | 0.0003 | - | | 0.8364 | 5400 | 0.0001 | - | | 0.8442 | 5450 | 0.0001 | - | | 0.8519 | 5500 | 0.0001 | - | | 0.8597 | 5550 | 0.0001 | - | | 0.8674 | 5600 | 0.0001 | - | | 0.8752 | 5650 | 0.0 | - | | 0.8829 | 5700 | 0.0003 | - | | 0.8906 | 5750 | 0.0003 | - | | 0.8984 | 5800 | 0.0001 | - | | 0.9061 | 5850 | 0.0001 | - | | 0.9139 | 5900 | 0.0002 | - | | 0.9216 | 5950 | 0.0 | - | | 0.9294 | 6000 | 0.0001 | - | | 0.9371 | 6050 | 0.0 | - | | 0.9449 | 6100 | 0.0 | - | | 0.9526 | 6150 | 0.0001 | - | | 0.9603 | 6200 | 0.0 | - | | 0.9681 | 6250 | 0.0001 | - | | 0.9758 | 6300 | 0.0002 | - | | 0.9836 | 6350 | 0.0 | - | | 0.9913 | 6400 | 0.0 | - | | 0.9991 | 6450 | 0.0002 | - | | **1.0** | **6456** | **-** | **1.3837** | | 1.0068 | 6500 | 0.0001 | - | | 1.0146 | 6550 | 0.0001 | - | | 1.0223 | 6600 | 0.0002 | - | | 1.0300 | 6650 | 0.0001 | - | | 1.0378 | 6700 | 0.0005 | - | | 1.0455 | 6750 | 0.0001 | - | | 1.0533 | 6800 | 0.0001 | - | | 1.0610 | 6850 | 0.0 | - | | 1.0688 | 6900 | 0.0 | - | | 1.0765 | 6950 | 0.0009 | - | | 1.0843 | 7000 | 0.0 | - | | 1.0920 | 7050 | 0.0032 | - | | 1.0998 | 7100 | 0.0001 | - | | 1.1075 | 7150 | 0.0001 | - | | 1.1152 | 7200 | 0.0001 | - | | 1.1230 | 7250 | 0.0 | - | | 1.1307 | 7300 | 0.0001 | - | | 1.1385 | 7350 | 0.0 | - | | 1.1462 | 7400 | 0.0 | - | | 1.1540 | 7450 | 0.0002 | - | | 1.1617 | 7500 | 0.0 | - | | 1.1695 | 7550 | 0.0427 | - | | 1.1772 | 7600 | 0.0 | - | | 1.1849 | 7650 | 0.0 | - | | 1.1927 | 7700 | 0.0 | - | | 1.2004 | 7750 | 0.0002 | - | | 1.2082 | 7800 | 0.0 | - | | 1.2159 | 7850 | 0.0 | - | | 1.2237 | 7900 | 0.0 | - | | 1.2314 | 7950 | 0.0 | - | | 1.2392 | 8000 | 0.0001 | - | | 1.2469 | 8050 | 0.0 | - | | 1.2546 | 8100 | 0.0001 | - | | 1.2624 | 8150 | 0.0 | - | | 1.2701 | 8200 | 0.0 | - | | 1.2779 | 8250 | 0.0 | - | | 1.2856 | 8300 | 0.0 | - | | 1.2934 | 8350 | 0.0 | - | | 1.3011 | 8400 | 0.0 | - | | 1.3089 | 8450 | 0.0 | - | | 1.3166 | 8500 | 0.0 | - | | 1.3243 | 8550 | 0.0001 | - | | 1.3321 | 8600 | 0.0 | - | | 1.3398 | 8650 | 0.0002 | - | | 1.3476 | 8700 | 0.0 | - | | 1.3553 | 8750 | 0.0006 | - | | 1.3631 | 8800 | 0.0 | - | | 1.3708 | 8850 | 0.0 | - | | 1.3786 | 8900 | 0.0001 | - | | 1.3863 | 8950 | 0.0 | - | | 1.3941 | 9000 | 0.0001 | - | | 1.4018 | 9050 | 0.0 | - | | 1.4095 | 9100 | 0.0002 | - | | 1.4173 | 9150 | 0.0 | - | | 1.4250 | 9200 | 0.0 | - | | 1.4328 | 9250 | 0.0 | - | | 1.4405 | 9300 | 0.0 | - | | 1.4483 | 9350 | 0.0 | - | | 1.4560 | 9400 | 0.0 | - | | 1.4638 | 9450 | 0.0 | - | | 1.4715 | 9500 | 0.0 | - | | 1.4792 | 9550 | 0.0 | - | | 1.4870 | 9600 | 0.0 | - | | 1.4947 | 9650 | 0.0005 | - | | 1.5025 | 9700 | 0.0 | - | | 1.5102 | 9750 | 0.0001 | - | | 1.5180 | 9800 | 0.0001 | - | | 1.5257 | 9850 | 0.0001 | - | | 1.5335 | 9900 | 0.0 | - | | 1.5412 | 9950 | 0.0 | - | | 1.5489 | 10000 | 0.0 | - | | 1.5567 | 10050 | 0.0 | - | | 1.5644 | 10100 | 0.0001 | - | | 1.5722 | 10150 | 0.0 | - | | 1.5799 | 10200 | 0.0002 | - | | 1.5877 | 10250 | 0.0001 | - | | 1.5954 | 10300 | 0.0005 | - | | 1.6032 | 10350 | 0.0 | - | | 1.6109 | 10400 | 0.0 | - | | 1.6186 | 10450 | 0.0003 | - | | 1.6264 | 10500 | 0.0002 | - | | 1.6341 | 10550 | 0.0 | - | | 1.6419 | 10600 | 0.0 | - | | 1.6496 | 10650 | 0.0001 | - | | 1.6574 | 10700 | 0.0002 | - | | 1.6651 | 10750 | 0.0002 | - | | 1.6729 | 10800 | 0.0054 | - | | 1.6806 | 10850 | 0.0005 | - | | 1.6884 | 10900 | 0.0001 | - | | 1.6961 | 10950 | 0.0 | - | | 1.7038 | 11000 | 0.0 | - | | 1.7116 | 11050 | 0.0001 | - | | 1.7193 | 11100 | 0.0001 | - | | 1.7271 | 11150 | 0.0 | - | | 1.7348 | 11200 | 0.0001 | - | | 1.7426 | 11250 | 0.0 | - | | 1.7503 | 11300 | 0.0001 | - | | 1.7581 | 11350 | 0.0004 | - | | 1.7658 | 11400 | 0.0 | - | | 1.7735 | 11450 | 0.0001 | - | | 1.7813 | 11500 | 0.0 | - | | 1.7890 | 11550 | 0.0 | - | | 1.7968 | 11600 | 0.0 | - | | 1.8045 | 11650 | 0.0 | - | | 1.8123 | 11700 | 0.0001 | - | | 1.8200 | 11750 | 0.0002 | - | | 1.8278 | 11800 | 0.0 | - | | 1.8355 | 11850 | 0.0001 | - | | 1.8432 | 11900 | 0.0 | - | | 1.8510 | 11950 | 0.0001 | - | | 1.8587 | 12000 | 0.0 | - | | 1.8665 | 12050 | 0.0 | - | | 1.8742 | 12100 | 0.0 | - | | 1.8820 | 12150 | 0.0001 | - | | 1.8897 | 12200 | 0.0 | - | | 1.8975 | 12250 | 0.0 | - | | 1.9052 | 12300 | 0.0 | - | | 1.9129 | 12350 | 0.0 | - | | 1.9207 | 12400 | 0.0 | - | | 1.9284 | 12450 | 0.0 | - | | 1.9362 | 12500 | 0.0 | - | | 1.9439 | 12550 | 0.0003 | - | | 1.9517 | 12600 | 0.0001 | - | | 1.9594 | 12650 | 0.0 | - | | 1.9672 | 12700 | 0.0001 | - | | 1.9749 | 12750 | 0.0 | - | | 1.9827 | 12800 | 0.0 | - | | 1.9904 | 12850 | 0.0 | - | | 1.9981 | 12900 | 0.0001 | - | | 2.0 | 12912 | - | 2.611 | | 2.0059 | 12950 | 0.0 | - | | 2.0136 | 13000 | 0.0001 | - | | 2.0214 | 13050 | 0.0001 | - | | 2.0291 | 13100 | 0.0 | - | | 2.0369 | 13150 | 0.0 | - | | 2.0446 | 13200 | 0.0001 | - | | 2.0524 | 13250 | 0.0 | - | | 2.0601 | 13300 | 0.0002 | - | | 2.0678 | 13350 | 0.0 | - | | 2.0756 | 13400 | 0.0 | - | | 2.0833 | 13450 | 0.0001 | - | | 2.0911 | 13500 | 0.0001 | - | | 2.0988 | 13550 | 0.0003 | - | | 2.1066 | 13600 | 0.0 | - | | 2.1143 | 13650 | 0.0001 | - | | 2.1221 | 13700 | 0.0001 | - | | 2.1298 | 13750 | 0.0001 | - | | 2.1375 | 13800 | 0.0001 | - | | 2.1453 | 13850 | 0.0 | - | | 2.1530 | 13900 | 0.0 | - | | 2.1608 | 13950 | 0.0 | - | | 2.1685 | 14000 | 0.0 | - | | 2.1763 | 14050 | 0.0 | - | | 2.1840 | 14100 | 0.0001 | - | | 2.1918 | 14150 | 0.0 | - | | 2.1995 | 14200 | 0.0 | - | | 2.2072 | 14250 | 0.0001 | - | | 2.2150 | 14300 | 0.0 | - | | 2.2227 | 14350 | 0.0 | - | | 2.2305 | 14400 | 0.0004 | - | | 2.2382 | 14450 | 0.0001 | - | | 2.2460 | 14500 | 0.0 | - | | 2.2537 | 14550 | 0.0003 | - | | 2.2615 | 14600 | 0.0 | - | | 2.2692 | 14650 | 0.0001 | - | | 2.2770 | 14700 | 0.0001 | - | | 2.2847 | 14750 | 0.0 | - | | 2.2924 | 14800 | 0.0 | - | | 2.3002 | 14850 | 0.0005 | - | | 2.3079 | 14900 | 0.0 | - | | 2.3157 | 14950 | 0.0002 | - | | 2.3234 | 15000 | 0.0 | - | | 2.3312 | 15050 | 0.0 | - | | 2.3389 | 15100 | 0.0001 | - | | 2.3467 | 15150 | 0.0001 | - | | 2.3544 | 15200 | 0.0002 | - | | 2.3621 | 15250 | 0.0001 | - | | 2.3699 | 15300 | 0.0 | - | | 2.3776 | 15350 | 0.0 | - | | 2.3854 | 15400 | 0.0002 | - | | 2.3931 | 15450 | 0.0003 | - | | 2.4009 | 15500 | 0.0 | - | | 2.4086 | 15550 | 0.0 | - | | 2.4164 | 15600 | 0.0 | - | | 2.4241 | 15650 | 0.0001 | - | | 2.4318 | 15700 | 0.0 | - | | 2.4396 | 15750 | 0.0 | - | | 2.4473 | 15800 | 0.0002 | - | | 2.4551 | 15850 | 0.0 | - | | 2.4628 | 15900 | 0.0 | - | | 2.4706 | 15950 | 0.0 | - | | 2.4783 | 16000 | 0.0 | - | | 2.4861 | 16050 | 0.0001 | - | | 2.4938 | 16100 | 0.0 | - | | 2.5015 | 16150 | 0.0 | - | | 2.5093 | 16200 | 0.0 | - | | 2.5170 | 16250 | 0.0 | - | | 2.5248 | 16300 | 0.0 | - | | 2.5325 | 16350 | 0.0 | - | | 2.5403 | 16400 | 0.0 | - | | 2.5480 | 16450 | 0.0 | - | | 2.5558 | 16500 | 0.0 | - | | 2.5635 | 16550 | 0.0001 | - | | 2.5713 | 16600 | 0.0 | - | | 2.5790 | 16650 | 0.0 | - | | 2.5867 | 16700 | 0.0 | - | | 2.5945 | 16750 | 0.0 | - | | 2.6022 | 16800 | 0.0009 | - | | 2.6100 | 16850 | 0.0001 | - | | 2.6177 | 16900 | 0.0 | - | | 2.6255 | 16950 | 0.0001 | - | | 2.6332 | 17000 | 0.0 | - | | 2.6410 | 17050 | 0.0 | - | | 2.6487 | 17100 | 0.0001 | - | | 2.6564 | 17150 | 0.0 | - | | 2.6642 | 17200 | 0.0 | - | | 2.6719 | 17250 | 0.0 | - | | 2.6797 | 17300 | 0.0 | - | | 2.6874 | 17350 | 0.0004 | - | | 2.6952 | 17400 | 0.0 | - | | 2.7029 | 17450 | 0.0 | - | | 2.7107 | 17500 | 0.0 | - | | 2.7184 | 17550 | 0.0 | - | | 2.7261 | 17600 | 0.0 | - | | 2.7339 | 17650 | 0.0 | - | | 2.7416 | 17700 | 0.0001 | - | | 2.7494 | 17750 | 0.0 | - | | 2.7571 | 17800 | 0.0 | - | | 2.7649 | 17850 | 0.0001 | - | | 2.7726 | 17900 | 0.0 | - | | 2.7804 | 17950 | 0.0001 | - | | 2.7881 | 18000 | 0.0001 | - | | 2.7958 | 18050 | 0.0 | - | | 2.8036 | 18100 | 0.0 | - | | 2.8113 | 18150 | 0.0 | - | | 2.8191 | 18200 | 0.0 | - | | 2.8268 | 18250 | 0.0 | - | | 2.8346 | 18300 | 0.0001 | - | | 2.8423 | 18350 | 0.0 | - | | 2.8501 | 18400 | 0.0 | - | | 2.8578 | 18450 | 0.0 | - | | 2.8656 | 18500 | 0.0 | - | | 2.8733 | 18550 | 0.0 | - | | 2.8810 | 18600 | 0.0 | - | | 2.8888 | 18650 | 0.0 | - | | 2.8965 | 18700 | 0.0 | - | | 2.9043 | 18750 | 0.0 | - | | 2.9120 | 18800 | 0.0001 | - | | 2.9198 | 18850 | 0.0 | - | | 2.9275 | 18900 | 0.0 | - | | 2.9353 | 18950 | 0.0 | - | | 2.9430 | 19000 | 0.0 | - | | 2.9507 | 19050 | 0.0 | - | | 2.9585 | 19100 | 0.0 | - | | 2.9662 | 19150 | 0.0 | - | | 2.9740 | 19200 | 0.0 | - | | 2.9817 | 19250 | 0.0003 | - | | 2.9895 | 19300 | 0.0001 | - | | 2.9972 | 19350 | 0.0 | - | | 3.0 | 19368 | - | 2.0845 | | 3.0050 | 19400 | 0.0 | - | | 3.0127 | 19450 | 0.0001 | - | | 3.0204 | 19500 | 0.0 | - | | 3.0282 | 19550 | 0.0 | - | | 3.0359 | 19600 | 0.0 | - | | 3.0437 | 19650 | 0.0 | - | | 3.0514 | 19700 | 0.0 | - | | 3.0592 | 19750 | 0.0 | - | | 3.0669 | 19800 | 0.0001 | - | | 3.0747 | 19850 | 0.0 | - | | 3.0824 | 19900 | 0.0 | - | | 3.0901 | 19950 | 0.0001 | - | | 3.0979 | 20000 | 0.0 | - | | 3.1056 | 20050 | 0.0 | - | | 3.1134 | 20100 | 0.0 | - | | 3.1211 | 20150 | 0.0001 | - | | 3.1289 | 20200 | 0.0 | - | | 3.1366 | 20250 | 0.0 | - | | 3.1444 | 20300 | 0.0 | - | | 3.1521 | 20350 | 0.0 | - | | 3.1599 | 20400 | 0.0 | - | | 3.1676 | 20450 | 0.0001 | - | | 3.1753 | 20500 | 0.0 | - | | 3.1831 | 20550 | 0.0001 | - | | 3.1908 | 20600 | 0.0 | - | | 3.1986 | 20650 | 0.0 | - | | 3.2063 | 20700 | 0.0 | - | | 3.2141 | 20750 | 0.0 | - | | 3.2218 | 20800 | 0.0 | - | | 3.2296 | 20850 | 0.0003 | - | | 3.2373 | 20900 | 0.0 | - | | 3.2450 | 20950 | 0.0 | - | | 3.2528 | 21000 | 0.0 | - | | 3.2605 | 21050 | 0.0 | - | | 3.2683 | 21100 | 0.0001 | - | | 3.2760 | 21150 | 0.0001 | - | | 3.2838 | 21200 | 0.0 | - | | 3.2915 | 21250 | 0.0 | - | | 3.2993 | 21300 | 0.0 | - | | 3.3070 | 21350 | 0.0 | - | | 3.3147 | 21400 | 0.0 | - | | 3.3225 | 21450 | 0.0001 | - | | 3.3302 | 21500 | 0.0 | - | | 3.3380 | 21550 | 0.0 | - | | 3.3457 | 21600 | 0.0 | - | | 3.3535 | 21650 | 0.0 | - | | 3.3612 | 21700 | 0.0 | - | | 3.3690 | 21750 | 0.0 | - | | 3.3767 | 21800 | 0.0 | - | | 3.3844 | 21850 | 0.0 | - | | 3.3922 | 21900 | 0.0001 | - | | 3.3999 | 21950 | 0.0 | - | | 3.4077 | 22000 | 0.0 | - | | 3.4154 | 22050 | 0.0001 | - | | 3.4232 | 22100 | 0.0 | - | | 3.4309 | 22150 | 0.0001 | - | | 3.4387 | 22200 | 0.0 | - | | 3.4464 | 22250 | 0.0 | - | | 3.4542 | 22300 | 0.0 | - | | 3.4619 | 22350 | 0.0001 | - | | 3.4696 | 22400 | 0.0 | - | | 3.4774 | 22450 | 0.0 | - | | 3.4851 | 22500 | 0.0 | - | | 3.4929 | 22550 | 0.0001 | - | | 3.5006 | 22600 | 0.0002 | - | | 3.5084 | 22650 | 0.0001 | - | | 3.5161 | 22700 | 0.0 | - | | 3.5239 | 22750 | 0.0001 | - | | 3.5316 | 22800 | 0.0 | - | | 3.5393 | 22850 | 0.0 | - | | 3.5471 | 22900 | 0.0001 | - | | 3.5548 | 22950 | 0.0 | - | | 3.5626 | 23000 | 0.0 | - | | 3.5703 | 23050 | 0.0 | - | | 3.5781 | 23100 | 0.0 | - | | 3.5858 | 23150 | 0.0001 | - | | 3.5936 | 23200 | 0.0 | - | | 3.6013 | 23250 | 0.0001 | - | | 3.6090 | 23300 | 0.0001 | - | | 3.6168 | 23350 | 0.0 | - | | 3.6245 | 23400 | 0.0003 | - | | 3.6323 | 23450 | 0.0 | - | | 3.6400 | 23500 | 0.0 | - | | 3.6478 | 23550 | 0.0001 | - | | 3.6555 | 23600 | 0.0 | - | | 3.6633 | 23650 | 0.0 | - | | 3.6710 | 23700 | 0.0 | - | | 3.6787 | 23750 | 0.0001 | - | | 3.6865 | 23800 | 0.0 | - | | 3.6942 | 23850 | 0.0001 | - | | 3.7020 | 23900 | 0.0002 | - | | 3.7097 | 23950 | 0.0 | - | | 3.7175 | 24000 | 0.0 | - | | 3.7252 | 24050 | 0.0 | - | | 3.7330 | 24100 | 0.0 | - | | 3.7407 | 24150 | 0.0001 | - | | 3.7485 | 24200 | 0.0 | - | | 3.7562 | 24250 | 0.0 | - | | 3.7639 | 24300 | 0.0 | - | | 3.7717 | 24350 | 0.0 | - | | 3.7794 | 24400 | 0.0 | - | | 3.7872 | 24450 | 0.0 | - | | 3.7949 | 24500 | 0.0001 | - | | 3.8027 | 24550 | 0.0001 | - | | 3.8104 | 24600 | 0.0 | - | | 3.8182 | 24650 | 0.0 | - | | 3.8259 | 24700 | 0.0 | - | | 3.8336 | 24750 | 0.0 | - | | 3.8414 | 24800 | 0.0001 | - | | 3.8491 | 24850 | 0.0 | - | | 3.8569 | 24900 | 0.0 | - | | 3.8646 | 24950 | 0.0 | - | | 3.8724 | 25000 | 0.0 | - | | 3.8801 | 25050 | 0.0 | - | | 3.8879 | 25100 | 0.0 | - | | 3.8956 | 25150 | 0.0001 | - | | 3.9033 | 25200 | 0.0 | - | | 3.9111 | 25250 | 0.0002 | - | | 3.9188 | 25300 | 0.0001 | - | | 3.9266 | 25350 | 0.0 | - | | 3.9343 | 25400 | 0.0 | - | | 3.9421 | 25450 | 0.0 | - | | 3.9498 | 25500 | 0.0001 | - | | 3.9576 | 25550 | 0.0 | - | | 3.9653 | 25600 | 0.0 | - | | 3.9730 | 25650 | 0.0001 | - | | 3.9808 | 25700 | 0.0 | - | | 3.9885 | 25750 | 0.0 | - | | 3.9963 | 25800 | 0.0 | - | | 4.0 | 25824 | - | 2.3576 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 3.0.1 - Transformers: 4.39.0 - PyTorch: 2.3.0+cu121 - Datasets: 2.20.0 - 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} } ```