diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,804 @@ +--- +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} +} +``` + + + + + + \ No newline at end of file