|
--- |
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base_model: BAAI/bge-base-en-v1.5 |
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library_name: setfit |
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metrics: |
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- f1 |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: Discussion on recent report publication |
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- text: Growth |
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- text: The roundtable was arranged in order to provide an overview of the work of |
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Alliance members and promote international development policy positions to the |
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Scottish Conservatives. During the meeting we presented the work of SCIAF and |
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its campaign for a world leading climate change response. In particular SCIAF |
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explained how climate change is already affecting some of the poorest communities |
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in the world and is therefore a central concern for international development. |
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We argued that Scotland needs to do what it can to mitigate climate change. |
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- text: To introduce Energy UK discuss the energy industries contribution to tackling |
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climate change and discuss stage 1 of theClimate Change (Emissions Reduction Targets) |
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(Scotland) Bill. Also discussed the Scottish Government's ambition on electric |
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vehicles and the role of the energy industry in a successful roll out. |
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- text: To discuss our key asks on the Climate Change (Emissions Reduction Targets) |
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(Scotland) Bill in advance of Stage 2 including support for amendments on regional |
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land use partnerships and land use strategy as means to deliver climate mitigation |
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for land. |
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inference: True |
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model-index: |
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- name: SetFit with BAAI/bge-base-en-v1.5 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.9667149059334297 |
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name: F1 |
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- type: accuracy |
|
value: 0.9420654911838791 |
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name: Accuracy |
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--- |
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|
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# SetFit with BAAI/bge-base-en-v1.5 |
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|
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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. |
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|
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The model has been trained using an efficient few-shot learning technique that involves: |
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|
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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|
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undefined = Health |
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1 = Housing |
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2 = Defence |
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3 = Climate |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
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- **Maximum Sequence Length:** 512 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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|
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## Evaluation |
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|
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### Metrics |
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| Label | F1 | Accuracy | |
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|:--------|:-------|:---------| |
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| **all** | 0.9667 | 0.9421 | |
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|
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## Uses |
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### Direct Use for Inference |
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|
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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|
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("twright8/setfit_lobbying_classifier") |
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# Run inference |
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preds = model("Growth") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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|
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 1 | 39.4538 | 282 | |
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|
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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- num_epochs: (4, 9) |
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- max_steps: -1 |
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- sampling_strategy: undersampling |
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- body_learning_rate: (1.0797496673911536e-05, 3.457046714445997e-05) |
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- head_learning_rate: 0.0004470582121407239 |
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- loss: CoSENTLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: True |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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|
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### Training Results |
|
| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:--------:|:-------------:|:---------------:| |
|
| 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} |
|
} |
|
``` |
|
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