--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer base_model: sentence-transformers/all-MiniLM-L6-v2 metrics: - accuracy widget: - text: No authorization or approval or other action by, and no notice to or filing with, any governmental authority or regulatory body is required for the due execution and delivery by the Servicer of this Agreement and each other Transaction Document to which it is a party and the performance of its obligations hereunder and thereunder in its capacity as Servicer. - text: All rights and remedies of Collateral Agent shall be cumulative and may be exercised singularly or concurrently, at their option, and the exercise or enforcement of any one such right or remedy shall not bar or be a condition to the exercise or enforcement of any other. - text: Except for the conveyances hereunder, Seller will not sell, pledge, assign or transfer to any other Person, or grant, create, incur, assume or suffer to exist any Lien on the Receivables or the Other Conveyed Property or any interest therein, and Seller shall defend the right, title, and interest of Purchaser and the Issuer in and to the Receivables and the Other Conveyed Property against all claims of third parties claiming through or under Seller. - text: In the event of a Change in Control, the Eligible Employee shall immediately be fully vested in his or her benefit under the Plan. - text: If Participant’s Employment terminates under circumstances described in Section 3(a) , then upon Participant’s subsequent death, all unpaid amounts payable to Participant under Section 3(a)(i) , (ii) , (iii)  or (vi) , if any, shall be paid to Participant’s Beneficiary. pipeline_tag: text-classification inference: true model-index: - name: SetFit with sentence-transformers/all-MiniLM-L6-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.9425 name: Accuracy --- # SetFit with sentence-transformers/all-MiniLM-L6-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 256 tokens - **Number of Classes:** 100 classes ### 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) ### Model Labels | Label | Examples | |:-----------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | governing laws | | | counterparts | | | notices | | | entire agreements | | | severability | | | waivers | | | amendments | | | expenses | | | survival | | | representations | | | assigns | | | taxes | | | litigations | | | insurances | | | confidentiality | | | waiver of jury trials | | | terminations | | | further assurances | | | general | | | terms | | | assignments | | | authority | | | use of proceeds | | | payments | | | compliance with laws | | | no conflicts | | | indemnifications | | | organizations | | | base salary | | | binding effects | | | headings | | | costs | | | definitions | | | modifications | | | remedies | | | releases | | | disclosures | | | participations | | | vesting | | | no waivers | | | withholdings | | | miscellaneous | | | jurisdictions | | | closings | | | integration | | | fees | | | effective dates | | | enforcements | | | financial statements | | | capitalization | | | benefits | | | interpretations | | | subsidiaries | | | solvency | | | cooperation | | | approvals | | | construction | | | intellectual property | | | brokers | | | enforceability | | | authorizations | | | consents | | | tax withholdings | | | arbitration | | | transactions with affiliates | | | applicable laws | | | defined terms | | | change in control | | | no defaults | | | adjustments | | | non-disparagement | | | employment | | | positions | | | erisa | | | warranties | | | disability | | | interests | | | duties | | | specific performance | | | anti-corruption laws | | | vacations | | | generally | | | publicity | | | choice of laws | | | liens | | | death | | | purposes | | | information | | | compensation | | | consent to jurisdiction | | | successors | | | limitation of liability | | | books | | | exercise price | | | register | | | powers | | | good standings | | | transferability | | | permits | | | existence | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.9425 | ## 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("scholarly360/setfit-contracts-clauses") # Run inference preds = model("In the event of a Change in Control, the Eligible Employee shall immediately be fully vested in his or her benefit under the Plan.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 8 | 48.2975 | 87 | | Label | Training Sample Count | |:-----------------------------|:----------------------| | governing laws | 4 | | counterparts | 4 | | notices | 4 | | entire agreements | 4 | | severability | 4 | | waivers | 4 | | amendments | 4 | | expenses | 4 | | survival | 4 | | representations | 4 | | assigns | 4 | | taxes | 4 | | litigations | 4 | | insurances | 4 | | confidentiality | 4 | | waiver of jury trials | 4 | | terminations | 4 | | further assurances | 4 | | general | 4 | | terms | 4 | | assignments | 4 | | authority | 4 | | use of proceeds | 4 | | payments | 4 | | compliance with laws | 4 | | no conflicts | 4 | | indemnifications | 4 | | organizations | 4 | | base salary | 4 | | binding effects | 4 | | headings | 4 | | costs | 4 | | definitions | 4 | | modifications | 4 | | remedies | 4 | | releases | 4 | | disclosures | 4 | | participations | 4 | | vesting | 4 | | no waivers | 4 | | withholdings | 4 | | miscellaneous | 4 | | jurisdictions | 4 | | closings | 4 | | integration | 4 | | fees | 4 | | effective dates | 4 | | enforcements | 4 | | financial statements | 4 | | capitalization | 4 | | benefits | 4 | | interpretations | 4 | | subsidiaries | 4 | | solvency | 4 | | cooperation | 4 | | approvals | 4 | | construction | 4 | | intellectual property | 4 | | brokers | 4 | | enforceability | 4 | | authorizations | 4 | | consents | 4 | | tax withholdings | 4 | | arbitration | 4 | | transactions with affiliates | 4 | | applicable laws | 4 | | defined terms | 4 | | change in control | 4 | | no defaults | 4 | | adjustments | 4 | | non-disparagement | 4 | | employment | 4 | | positions | 4 | | erisa | 4 | | warranties | 4 | | disability | 4 | | interests | 4 | | duties | 4 | | specific performance | 4 | | anti-corruption laws | 4 | | vacations | 4 | | generally | 4 | | publicity | 4 | | choice of laws | 4 | | liens | 4 | | death | 4 | | purposes | 4 | | information | 4 | | compensation | 4 | | consent to jurisdiction | 4 | | successors | 4 | | limitation of liability | 4 | | books | 4 | | exercise price | 4 | | register | 4 | | powers | 4 | | good standings | 4 | | transferability | 4 | | permits | 4 | | existence | 4 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (2, 2) - 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 | |:-------:|:---------:|:-------------:|:---------------:| | 0.0001 | 1 | 0.1159 | - | | 0.0051 | 50 | 0.1675 | - | | 0.0101 | 100 | 0.1142 | - | | 0.0152 | 150 | 0.1509 | - | | 0.0202 | 200 | 0.0455 | - | | 0.0253 | 250 | 0.0999 | - | | 0.0303 | 300 | 0.1259 | - | | 0.0354 | 350 | 0.0873 | - | | 0.0404 | 400 | 0.0993 | - | | 0.0455 | 450 | 0.0457 | - | | 0.0505 | 500 | 0.0835 | - | | 0.0556 | 550 | 0.0809 | - | | 0.0606 | 600 | 0.0821 | - | | 0.0657 | 650 | 0.0603 | - | | 0.0707 | 700 | 0.0502 | - | | 0.0758 | 750 | 0.0532 | - | | 0.0808 | 800 | 0.06 | - | | 0.0859 | 850 | 0.1101 | - | | 0.0909 | 900 | 0.036 | - | | 0.0960 | 950 | 0.0287 | - | | 0.1010 | 1000 | 0.0501 | - | | 0.1061 | 1050 | 0.0405 | - | | 0.1111 | 1100 | 0.0327 | - | | 0.1162 | 1150 | 0.0315 | - | | 0.1212 | 1200 | 0.022 | - | | 0.1263 | 1250 | 0.0346 | - | | 0.1313 | 1300 | 0.0782 | - | | 0.1364 | 1350 | 0.0353 | - | | 0.1414 | 1400 | 0.0225 | - | | 0.1465 | 1450 | 0.0134 | - | | 0.1515 | 1500 | 0.0791 | - | | 0.1566 | 1550 | 0.015 | - | | 0.1616 | 1600 | 0.0093 | - | | 0.1667 | 1650 | 0.024 | - | | 0.1717 | 1700 | 0.0062 | - | | 0.1768 | 1750 | 0.0245 | - | | 0.1818 | 1800 | 0.0102 | - | | 0.1869 | 1850 | 0.0086 | - | | 0.1919 | 1900 | 0.0238 | - | | 0.1970 | 1950 | 0.0062 | - | | 0.2020 | 2000 | 0.0382 | - | | 0.2071 | 2050 | 0.0107 | - | | 0.2121 | 2100 | 0.0045 | - | | 0.2172 | 2150 | 0.009 | - | | 0.2222 | 2200 | 0.0062 | - | | 0.2273 | 2250 | 0.0217 | - | | 0.2323 | 2300 | 0.0089 | - | | 0.2374 | 2350 | 0.0048 | - | | 0.2424 | 2400 | 0.0095 | - | | 0.2475 | 2450 | 0.0137 | - | | 0.2525 | 2500 | 0.0077 | - | | 0.2576 | 2550 | 0.0086 | - | | 0.2626 | 2600 | 0.0068 | - | | 0.2677 | 2650 | 0.0063 | - | | 0.2727 | 2700 | 0.0061 | - | | 0.2778 | 2750 | 0.0181 | - | | 0.2828 | 2800 | 0.0058 | - | | 0.2879 | 2850 | 0.0052 | - | | 0.2929 | 2900 | 0.0073 | - | | 0.2980 | 2950 | 0.0088 | - | | 0.3030 | 3000 | 0.0388 | - | | 0.3081 | 3050 | 0.0108 | - | | 0.3131 | 3100 | 0.0048 | - | | 0.3182 | 3150 | 0.0046 | - | | 0.3232 | 3200 | 0.0051 | - | | 0.3283 | 3250 | 0.0035 | - | | 0.3333 | 3300 | 0.0047 | - | | 0.3384 | 3350 | 0.0061 | - | | 0.3434 | 3400 | 0.0073 | - | | 0.3485 | 3450 | 0.0041 | - | | 0.3535 | 3500 | 0.0117 | - | | 0.3586 | 3550 | 0.0032 | - | | 0.3636 | 3600 | 0.0045 | - | | 0.3687 | 3650 | 0.0042 | - | | 0.3737 | 3700 | 0.0061 | - | | 0.3788 | 3750 | 0.0056 | - | | 0.3838 | 3800 | 0.0073 | - | | 0.3889 | 3850 | 0.0057 | - | | 0.3939 | 3900 | 0.0033 | - | | 0.3990 | 3950 | 0.0027 | - | | 0.4040 | 4000 | 0.0057 | - | | 0.4091 | 4050 | 0.003 | - | | 0.4141 | 4100 | 0.0044 | - | | 0.4192 | 4150 | 0.0033 | - | | 0.4242 | 4200 | 0.0036 | - | | 0.4293 | 4250 | 0.0027 | - | | 0.4343 | 4300 | 0.0065 | - | | 0.4394 | 4350 | 0.035 | - | | 0.4444 | 4400 | 0.0175 | - | | 0.4495 | 4450 | 0.0027 | - | | 0.4545 | 4500 | 0.0035 | - | | 0.4596 | 4550 | 0.0019 | - | | 0.4646 | 4600 | 0.0036 | - | | 0.4697 | 4650 | 0.0022 | - | | 0.4747 | 4700 | 0.0018 | - | | 0.4798 | 4750 | 0.0076 | - | | 0.4848 | 4800 | 0.0036 | - | | 0.4899 | 4850 | 0.0581 | - | | 0.4949 | 4900 | 0.0023 | - | | 0.5 | 4950 | 0.004 | - | | 0.5051 | 5000 | 0.0059 | - | | 0.5101 | 5050 | 0.0024 | - | | 0.5152 | 5100 | 0.0096 | - | | 0.5202 | 5150 | 0.0059 | - | | 0.5253 | 5200 | 0.0044 | - | | 0.5303 | 5250 | 0.041 | - | | 0.5354 | 5300 | 0.0028 | - | | 0.5404 | 5350 | 0.0032 | - | | 0.5455 | 5400 | 0.0017 | - | | 0.5505 | 5450 | 0.002 | - | | 0.5556 | 5500 | 0.0024 | - | | 0.5606 | 5550 | 0.0034 | - | | 0.5657 | 5600 | 0.0039 | - | | 0.5707 | 5650 | 0.0023 | - | | 0.5758 | 5700 | 0.0037 | - | | 0.5808 | 5750 | 0.0594 | - | | 0.5859 | 5800 | 0.0016 | - | | 0.5909 | 5850 | 0.0168 | - | | 0.5960 | 5900 | 0.0458 | - | | 0.6010 | 5950 | 0.0019 | - | | 0.6061 | 6000 | 0.001 | - | | 0.6111 | 6050 | 0.0294 | - | | 0.6162 | 6100 | 0.0027 | - | | 0.6212 | 6150 | 0.0051 | - | | 0.6263 | 6200 | 0.0014 | - | | 0.6313 | 6250 | 0.0033 | - | | 0.6364 | 6300 | 0.0021 | - | | 0.6414 | 6350 | 0.0023 | - | | 0.6465 | 6400 | 0.0018 | - | | 0.6515 | 6450 | 0.0013 | - | | 0.6566 | 6500 | 0.0041 | - | | 0.6616 | 6550 | 0.0592 | - | | 0.6667 | 6600 | 0.0019 | - | | 0.6717 | 6650 | 0.0021 | - | | 0.6768 | 6700 | 0.0606 | - | | 0.6818 | 6750 | 0.0018 | - | | 0.6869 | 6800 | 0.0014 | - | | 0.6919 | 6850 | 0.0038 | - | | 0.6970 | 6900 | 0.0567 | - | | 0.7020 | 6950 | 0.0013 | - | | 0.7071 | 7000 | 0.0015 | - | | 0.7121 | 7050 | 0.0585 | - | | 0.7172 | 7100 | 0.0014 | - | | 0.7222 | 7150 | 0.0021 | - | | 0.7273 | 7200 | 0.0179 | - | | 0.7323 | 7250 | 0.0013 | - | | 0.7374 | 7300 | 0.0101 | - | | 0.7424 | 7350 | 0.0012 | - | | 0.7475 | 7400 | 0.0009 | - | | 0.7525 | 7450 | 0.001 | - | | 0.7576 | 7500 | 0.0011 | - | | 0.7626 | 7550 | 0.001 | - | | 0.7677 | 7600 | 0.0022 | - | | 0.7727 | 7650 | 0.0012 | - | | 0.7778 | 7700 | 0.0011 | - | | 0.7828 | 7750 | 0.0011 | - | | 0.7879 | 7800 | 0.0011 | - | | 0.7929 | 7850 | 0.0019 | - | | 0.7980 | 7900 | 0.001 | - | | 0.8030 | 7950 | 0.0594 | - | | 0.8081 | 8000 | 0.024 | - | | 0.8131 | 8050 | 0.001 | - | | 0.8182 | 8100 | 0.0017 | - | | 0.8232 | 8150 | 0.0013 | - | | 0.8283 | 8200 | 0.0012 | - | | 0.8333 | 8250 | 0.0017 | - | | 0.8384 | 8300 | 0.0011 | - | | 0.8434 | 8350 | 0.0013 | - | | 0.8485 | 8400 | 0.0008 | - | | 0.8535 | 8450 | 0.0007 | - | | 0.8586 | 8500 | 0.0016 | - | | 0.8636 | 8550 | 0.0008 | - | | 0.8687 | 8600 | 0.0507 | - | | 0.8737 | 8650 | 0.0014 | - | | 0.8788 | 8700 | 0.0009 | - | | 0.8838 | 8750 | 0.0564 | - | | 0.8889 | 8800 | 0.001 | - | | 0.8939 | 8850 | 0.0016 | - | | 0.8990 | 8900 | 0.001 | - | | 0.9040 | 8950 | 0.0009 | - | | 0.9091 | 9000 | 0.0009 | - | | 0.9141 | 9050 | 0.0014 | - | | 0.9192 | 9100 | 0.0018 | - | | 0.9242 | 9150 | 0.0012 | - | | 0.9293 | 9200 | 0.0007 | - | | 0.9343 | 9250 | 0.0009 | - | | 0.9394 | 9300 | 0.0007 | - | | 0.9444 | 9350 | 0.0014 | - | | 0.9495 | 9400 | 0.0554 | - | | 0.9545 | 9450 | 0.001 | - | | 0.9596 | 9500 | 0.0011 | - | | 0.9646 | 9550 | 0.0008 | - | | 0.9697 | 9600 | 0.0008 | - | | 0.9747 | 9650 | 0.0012 | - | | 0.9798 | 9700 | 0.001 | - | | 0.9848 | 9750 | 0.0168 | - | | 0.9899 | 9800 | 0.0011 | - | | 0.9949 | 9850 | 0.0011 | - | | 1.0 | 9900 | 0.0194 | 0.0034 | | 1.0051 | 9950 | 0.0546 | - | | 1.0101 | 10000 | 0.0482 | - | | 1.0152 | 10050 | 0.0009 | - | | 1.0202 | 10100 | 0.0008 | - | | 1.0253 | 10150 | 0.0006 | - | | 1.0303 | 10200 | 0.0006 | - | | 1.0354 | 10250 | 0.0446 | - | | 1.0404 | 10300 | 0.0005 | - | | 1.0455 | 10350 | 0.0008 | - | | 1.0505 | 10400 | 0.0006 | - | | 1.0556 | 10450 | 0.0009 | - | | 1.0606 | 10500 | 0.0014 | - | | 1.0657 | 10550 | 0.0006 | - | | 1.0707 | 10600 | 0.0009 | - | | 1.0758 | 10650 | 0.0005 | - | | 1.0808 | 10700 | 0.0008 | - | | 1.0859 | 10750 | 0.0545 | - | | 1.0909 | 10800 | 0.0015 | - | | 1.0960 | 10850 | 0.0006 | - | | 1.1010 | 10900 | 0.0103 | - | | 1.1061 | 10950 | 0.001 | - | | 1.1111 | 11000 | 0.0011 | - | | 1.1162 | 11050 | 0.0009 | - | | 1.1212 | 11100 | 0.0014 | - | | 1.1263 | 11150 | 0.0011 | - | | 1.1313 | 11200 | 0.0007 | - | | 1.1364 | 11250 | 0.0025 | - | | 1.1414 | 11300 | 0.0007 | - | | 1.1465 | 11350 | 0.0007 | - | | 1.1515 | 11400 | 0.0584 | - | | 1.1566 | 11450 | 0.0008 | - | | 1.1616 | 11500 | 0.0007 | - | | 1.1667 | 11550 | 0.0005 | - | | 1.1717 | 11600 | 0.0009 | - | | 1.1768 | 11650 | 0.0005 | - | | 1.1818 | 11700 | 0.0009 | - | | 1.1869 | 11750 | 0.0008 | - | | 1.1919 | 11800 | 0.0009 | - | | 1.1970 | 11850 | 0.0007 | - | | 1.2020 | 11900 | 0.0006 | - | | 1.2071 | 11950 | 0.0006 | - | | 1.2121 | 12000 | 0.0005 | - | | 1.2172 | 12050 | 0.0008 | - | | 1.2222 | 12100 | 0.0006 | - | | 1.2273 | 12150 | 0.0004 | - | | 1.2323 | 12200 | 0.0006 | - | | 1.2374 | 12250 | 0.0005 | - | | 1.2424 | 12300 | 0.0005 | - | | 1.2475 | 12350 | 0.001 | - | | 1.2525 | 12400 | 0.0006 | - | | 1.2576 | 12450 | 0.0008 | - | | 1.2626 | 12500 | 0.0004 | - | | 1.2677 | 12550 | 0.0006 | - | | 1.2727 | 12600 | 0.001 | - | | 1.2778 | 12650 | 0.0005 | - | | 1.2828 | 12700 | 0.0005 | - | | 1.2879 | 12750 | 0.0006 | - | | 1.2929 | 12800 | 0.0005 | - | | 1.2980 | 12850 | 0.0011 | - | | 1.3030 | 12900 | 0.0011 | - | | 1.3081 | 12950 | 0.0006 | - | | 1.3131 | 13000 | 0.0006 | - | | 1.3182 | 13050 | 0.0006 | - | | 1.3232 | 13100 | 0.001 | - | | 1.3283 | 13150 | 0.0008 | - | | 1.3333 | 13200 | 0.0006 | - | | 1.3384 | 13250 | 0.0006 | - | | 1.3434 | 13300 | 0.0006 | - | | 1.3485 | 13350 | 0.0008 | - | | 1.3535 | 13400 | 0.001 | - | | 1.3586 | 13450 | 0.0006 | - | | 1.3636 | 13500 | 0.001 | - | | 1.3687 | 13550 | 0.0006 | - | | 1.3737 | 13600 | 0.0026 | - | | 1.3788 | 13650 | 0.0005 | - | | 1.3838 | 13700 | 0.0006 | - | | 1.3889 | 13750 | 0.0011 | - | | 1.3939 | 13800 | 0.0006 | - | | 1.3990 | 13850 | 0.0009 | - | | 1.4040 | 13900 | 0.0008 | - | | 1.4091 | 13950 | 0.0014 | - | | 1.4141 | 14000 | 0.0006 | - | | 1.4192 | 14050 | 0.0005 | - | | 1.4242 | 14100 | 0.0012 | - | | 1.4293 | 14150 | 0.0005 | - | | 1.4343 | 14200 | 0.0027 | - | | 1.4394 | 14250 | 0.0004 | - | | 1.4444 | 14300 | 0.0006 | - | | 1.4495 | 14350 | 0.001 | - | | 1.4545 | 14400 | 0.0004 | - | | 1.4596 | 14450 | 0.0005 | - | | 1.4646 | 14500 | 0.0004 | - | | 1.4697 | 14550 | 0.0005 | - | | 1.4747 | 14600 | 0.0008 | - | | 1.4798 | 14650 | 0.0004 | - | | 1.4848 | 14700 | 0.0005 | - | | 1.4899 | 14750 | 0.0581 | - | | 1.4949 | 14800 | 0.0005 | - | | 1.5 | 14850 | 0.001 | - | | 1.5051 | 14900 | 0.0007 | - | | 1.5101 | 14950 | 0.0004 | - | | 1.5152 | 15000 | 0.001 | - | | 1.5202 | 15050 | 0.0004 | - | | 1.5253 | 15100 | 0.0009 | - | | 1.5303 | 15150 | 0.0004 | - | | 1.5354 | 15200 | 0.0006 | - | | 1.5404 | 15250 | 0.0007 | - | | 1.5455 | 15300 | 0.0004 | - | | 1.5505 | 15350 | 0.0009 | - | | 1.5556 | 15400 | 0.0005 | - | | 1.5606 | 15450 | 0.0007 | - | | 1.5657 | 15500 | 0.0005 | - | | 1.5707 | 15550 | 0.0005 | - | | 1.5758 | 15600 | 0.0006 | - | | 1.5808 | 15650 | 0.0586 | - | | 1.5859 | 15700 | 0.0005 | - | | 1.5909 | 15750 | 0.0014 | - | | 1.5960 | 15800 | 0.0005 | - | | 1.6010 | 15850 | 0.0007 | - | | 1.6061 | 15900 | 0.0006 | - | | 1.6111 | 15950 | 0.0011 | - | | 1.6162 | 16000 | 0.0005 | - | | 1.6212 | 16050 | 0.0007 | - | | 1.6263 | 16100 | 0.0008 | - | | 1.6313 | 16150 | 0.0005 | - | | 1.6364 | 16200 | 0.0003 | - | | 1.6414 | 16250 | 0.0004 | - | | 1.6465 | 16300 | 0.0003 | - | | 1.6515 | 16350 | 0.0004 | - | | 1.6566 | 16400 | 0.0006 | - | | 1.6616 | 16450 | 0.0572 | - | | 1.6667 | 16500 | 0.0004 | - | | 1.6717 | 16550 | 0.0005 | - | | 1.6768 | 16600 | 0.0004 | - | | 1.6818 | 16650 | 0.0007 | - | | 1.6869 | 16700 | 0.0011 | - | | 1.6919 | 16750 | 0.0007 | - | | 1.6970 | 16800 | 0.0568 | - | | 1.7020 | 16850 | 0.0007 | - | | 1.7071 | 16900 | 0.0005 | - | | 1.7121 | 16950 | 0.0584 | - | | 1.7172 | 17000 | 0.0004 | - | | 1.7222 | 17050 | 0.0004 | - | | 1.7273 | 17100 | 0.0265 | - | | 1.7323 | 17150 | 0.0006 | - | | 1.7374 | 17200 | 0.0009 | - | | 1.7424 | 17250 | 0.0005 | - | | 1.7475 | 17300 | 0.0011 | - | | 1.7525 | 17350 | 0.0005 | - | | 1.7576 | 17400 | 0.0004 | - | | 1.7626 | 17450 | 0.0007 | - | | 1.7677 | 17500 | 0.0007 | - | | 1.7727 | 17550 | 0.0003 | - | | 1.7778 | 17600 | 0.0005 | - | | 1.7828 | 17650 | 0.0003 | - | | 1.7879 | 17700 | 0.0003 | - | | 1.7929 | 17750 | 0.0003 | - | | 1.7980 | 17800 | 0.0007 | - | | 1.8030 | 17850 | 0.0577 | - | | 1.8081 | 17900 | 0.0004 | - | | 1.8131 | 17950 | 0.0005 | - | | 1.8182 | 18000 | 0.0004 | - | | 1.8232 | 18050 | 0.0004 | - | | 1.8283 | 18100 | 0.0004 | - | | 1.8333 | 18150 | 0.0004 | - | | 1.8384 | 18200 | 0.0003 | - | | 1.8434 | 18250 | 0.0005 | - | | 1.8485 | 18300 | 0.0004 | - | | 1.8535 | 18350 | 0.0004 | - | | 1.8586 | 18400 | 0.0005 | - | | 1.8636 | 18450 | 0.0004 | - | | 1.8687 | 18500 | 0.0003 | - | | 1.8737 | 18550 | 0.0003 | - | | 1.8788 | 18600 | 0.0007 | - | | 1.8838 | 18650 | 0.0586 | - | | 1.8889 | 18700 | 0.0003 | - | | 1.8939 | 18750 | 0.0004 | - | | 1.8990 | 18800 | 0.0005 | - | | 1.9040 | 18850 | 0.0004 | - | | 1.9091 | 18900 | 0.0006 | - | | 1.9141 | 18950 | 0.0004 | - | | 1.9192 | 19000 | 0.0004 | - | | 1.9242 | 19050 | 0.0004 | - | | 1.9293 | 19100 | 0.0005 | - | | 1.9343 | 19150 | 0.0003 | - | | 1.9394 | 19200 | 0.0003 | - | | 1.9444 | 19250 | 0.0003 | - | | 1.9495 | 19300 | 0.0545 | - | | 1.9545 | 19350 | 0.0004 | - | | 1.9596 | 19400 | 0.0005 | - | | 1.9646 | 19450 | 0.0004 | - | | 1.9697 | 19500 | 0.0004 | - | | 1.9747 | 19550 | 0.0004 | - | | 1.9798 | 19600 | 0.0004 | - | | 1.9848 | 19650 | 0.0045 | - | | 1.9899 | 19700 | 0.0004 | - | | 1.9949 | 19750 | 0.0005 | - | | **2.0** | **19800** | **0.0006** | **0.0024** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.7.0 - Transformers: 4.40.2 - PyTorch: 2.2.1+cu121 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## 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} } ```