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---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: What tables are included in the starhub_data_asset database that relate to
customer complaints?
- text: What are the tables that I can access in the starhub_data_asset database?
- text: Can I have avg Cost_Efficiency
- text: Analyze product category revenue impact.
- text: Retrieve data_asset_kpi_ma_product details.
inference: true
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9914529914529915
name: Accuracy
---
# SetFit with BAAI/bge-small-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 7 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:-------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Generalreply | <ul><li>'Can you recommend a good movie to watch?'</li><li>"Oh, that's a tough one! There are so many good memories to choose from. But if I had to pick just one, I think it would be spending summers at my grandparent's house. We would play board games, make homemade ice cream, and have big family dinners. It was always so much fun!"</li><li>'Oh, I love reading books! My favorite genre is definitely fantasy. How about you? What kind of books do you like to read?'</li></ul> |
| Lookup_1 | <ul><li>'Get me data_asset_kpi_cf cash flow.'</li><li>'Display data_asset_001_pcc for electronics category.'</li><li>'Calculate Gross Profit Margin Trends.'</li></ul> |
| Lookup | <ul><li>"What are the products in the 'Clothing' category?"</li><li>"Get me the phone numbers of customers with the last name 'Johnson'."</li><li>"Can you filter by employees who have the last name 'Brown'?"</li></ul> |
| Aggregation | <ul><li>'Get me max Accumulated Amortisation and Impairment.'</li><li>'Can I have mode of Revenue'</li><li>'Show me count company_name'</li></ul> |
| Tablejoin | <ul><li>'Could you merge the Orders and Employees tables to identify which employees have processed high-value orders?'</li><li>'Could you connect the Products and Orders tables to analyze sales data by product category?'</li><li>'How can I connect the Customers and Orders tables to find customers who made purchases during a specific promotion?'</li></ul> |
| Viewtables | <ul><li>'What are the tables in the starhub_data_asset database that a user can join to perform a sales analysis?'</li><li>'What tables can be found in the asset-tracking section of the starhub_data_asset database?'</li><li>'What tables exist in the starhub_data_asset database?'</li></ul> |
| Rejection | <ul><li>"Let's avoid creating any new data sets."</li><li>"I'd prefer to avoid generating data fields."</li><li>"I'm not interested in filtering this collection."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9915 |
## 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("nazhan/bge-small-en-v1.5-brahmaputra-iter-10")
# Run inference
preds = model("Can I have avg Cost_Efficiency")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 8.6563 | 62 |
| Label | Training Sample Count |
|:-------------|:----------------------|
| Tablejoin | 129 |
| Rejection | 77 |
| Aggregation | 282 |
| Lookup | 60 |
| Generalreply | 63 |
| Viewtables | 74 |
| Lookup_1 | 150 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:-------:|:---------:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2038 | - |
| 0.0014 | 50 | 0.2019 | - |
| 0.0029 | 100 | 0.1983 | - |
| 0.0043 | 150 | 0.206 | - |
| 0.0057 | 200 | 0.2268 | - |
| 0.0071 | 250 | 0.2025 | - |
| 0.0086 | 300 | 0.2041 | - |
| 0.0100 | 350 | 0.1426 | - |
| 0.0114 | 400 | 0.1513 | - |
| 0.0129 | 450 | 0.1215 | - |
| 0.0143 | 500 | 0.1426 | - |
| 0.0157 | 550 | 0.0859 | - |
| 0.0172 | 600 | 0.0486 | - |
| 0.0186 | 650 | 0.0378 | - |
| 0.0200 | 700 | 0.0519 | - |
| 0.0214 | 750 | 0.0717 | - |
| 0.0229 | 800 | 0.1161 | - |
| 0.0243 | 850 | 0.0771 | - |
| 0.0257 | 900 | 0.074 | - |
| 0.0272 | 950 | 0.0567 | - |
| 0.0286 | 1000 | 0.0223 | - |
| 0.0300 | 1050 | 0.0266 | - |
| 0.0315 | 1100 | 0.0261 | - |
| 0.0329 | 1150 | 0.0333 | - |
| 0.0343 | 1200 | 0.0107 | - |
| 0.0357 | 1250 | 0.0123 | - |
| 0.0372 | 1300 | 0.0193 | - |
| 0.0386 | 1350 | 0.0039 | - |
| 0.0400 | 1400 | 0.0079 | - |
| 0.0415 | 1450 | 0.0035 | - |
| 0.0429 | 1500 | 0.003 | - |
| 0.0443 | 1550 | 0.0041 | - |
| 0.0457 | 1600 | 0.0038 | - |
| 0.0472 | 1650 | 0.002 | - |
| 0.0486 | 1700 | 0.0028 | - |
| 0.0500 | 1750 | 0.0056 | - |
| 0.0515 | 1800 | 0.0035 | - |
| 0.0529 | 1850 | 0.0027 | - |
| 0.0543 | 1900 | 0.0028 | - |
| 0.0558 | 1950 | 0.0028 | - |
| 0.0572 | 2000 | 0.0019 | - |
| 0.0586 | 2050 | 0.0046 | - |
| 0.0600 | 2100 | 0.0017 | - |
| 0.0615 | 2150 | 0.0016 | - |
| 0.0629 | 2200 | 0.0022 | - |
| 0.0643 | 2250 | 0.002 | - |
| 0.0658 | 2300 | 0.0029 | - |
| 0.0672 | 2350 | 0.0032 | - |
| 0.0686 | 2400 | 0.0018 | - |
| 0.0701 | 2450 | 0.0015 | - |
| 0.0715 | 2500 | 0.0015 | - |
| 0.0729 | 2550 | 0.0016 | - |
| 0.0743 | 2600 | 0.0012 | - |
| 0.0758 | 2650 | 0.0014 | - |
| 0.0772 | 2700 | 0.0015 | - |
| 0.0786 | 2750 | 0.0018 | - |
| 0.0801 | 2800 | 0.0012 | - |
| 0.0815 | 2850 | 0.0009 | - |
| 0.0829 | 2900 | 0.001 | - |
| 0.0843 | 2950 | 0.0011 | - |
| 0.0858 | 3000 | 0.0011 | - |
| 0.0872 | 3050 | 0.001 | - |
| 0.0886 | 3100 | 0.0012 | - |
| 0.0901 | 3150 | 0.0006 | - |
| 0.0915 | 3200 | 0.0013 | - |
| 0.0929 | 3250 | 0.0007 | - |
| 0.0944 | 3300 | 0.0007 | - |
| 0.0958 | 3350 | 0.0009 | - |
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| 0.1001 | 3500 | 0.001 | - |
| 0.1015 | 3550 | 0.001 | - |
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| 0.1101 | 3850 | 0.0007 | - |
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| 0.1129 | 3950 | 0.0009 | - |
| 0.1144 | 4000 | 0.0007 | - |
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| 0.1215 | 4250 | 0.0011 | - |
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| 0.4174 | 14600 | 0.0002 | - |
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| 0.4932 | 17250 | 0.0002 | - |
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| 0.5089 | 17800 | 0.0002 | - |
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| 0.5118 | 17900 | 0.0002 | - |
| 0.5132 | 17950 | 0.0003 | - |
| 0.5147 | 18000 | 0.0002 | - |
| 0.5161 | 18050 | 0.0002 | - |
| 0.5175 | 18100 | 0.0002 | - |
| 0.5189 | 18150 | 0.0002 | - |
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| 0.5232 | 18300 | 0.0002 | - |
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| 0.5261 | 18400 | 0.0002 | - |
| 0.5275 | 18450 | 0.0003 | - |
| 0.5289 | 18500 | 0.0001 | - |
| 0.5304 | 18550 | 0.0002 | - |
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| 0.5332 | 18650 | 0.0002 | - |
| 0.5347 | 18700 | 0.0002 | - |
| 0.5361 | 18750 | 0.0002 | - |
| 0.5375 | 18800 | 0.0002 | - |
| 0.5390 | 18850 | 0.0001 | - |
| 0.5404 | 18900 | 0.0001 | - |
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| 0.5475 | 19150 | 0.0002 | - |
| 0.5490 | 19200 | 0.0002 | - |
| 0.5504 | 19250 | 0.0002 | - |
| 0.5518 | 19300 | 0.0001 | - |
| 0.5533 | 19350 | 0.0002 | - |
| 0.5547 | 19400 | 0.0002 | - |
| 0.5561 | 19450 | 0.0004 | - |
| 0.5575 | 19500 | 0.0002 | - |
| 0.5590 | 19550 | 0.0002 | - |
| 0.5604 | 19600 | 0.0003 | - |
| 0.5618 | 19650 | 0.0003 | - |
| 0.5633 | 19700 | 0.0002 | - |
| 0.5647 | 19750 | 0.0002 | - |
| 0.5661 | 19800 | 0.0001 | - |
| 0.5675 | 19850 | 0.0003 | - |
| 0.5690 | 19900 | 0.0002 | - |
| 0.5704 | 19950 | 0.0002 | - |
| 0.5718 | 20000 | 0.0001 | - |
| 0.5733 | 20050 | 0.0003 | - |
| 0.5747 | 20100 | 0.0001 | - |
| 0.5761 | 20150 | 0.0002 | - |
| 0.5776 | 20200 | 0.0003 | - |
| 0.5790 | 20250 | 0.0003 | - |
| 0.5804 | 20300 | 0.0002 | - |
| 0.5818 | 20350 | 0.0003 | - |
| 0.5833 | 20400 | 0.0002 | - |
| 0.5847 | 20450 | 0.0002 | - |
| 0.5861 | 20500 | 0.0002 | - |
| 0.5876 | 20550 | 0.0001 | - |
| 0.5890 | 20600 | 0.0002 | - |
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| 0.5919 | 20700 | 0.0002 | - |
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| 0.5961 | 20850 | 0.0001 | - |
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| 0.5990 | 20950 | 0.0001 | - |
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| 0.6133 | 21450 | 0.0002 | - |
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| 0.6204 | 21700 | 0.0001 | - |
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| 0.6233 | 21800 | 0.0001 | - |
| 0.6247 | 21850 | 0.0001 | - |
| 0.6262 | 21900 | 0.0001 | - |
| 0.6276 | 21950 | 0.0002 | - |
| 0.6290 | 22000 | 0.0002 | - |
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| 0.6347 | 22200 | 0.0001 | - |
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| 0.6390 | 22350 | 0.0001 | - |
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| 0.6576 | 23000 | 0.0002 | - |
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| 0.6633 | 23200 | 0.0002 | - |
| 0.6648 | 23250 | 0.0002 | - |
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| 0.6676 | 23350 | 0.0001 | - |
| 0.6690 | 23400 | 0.0002 | - |
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| 0.6819 | 23850 | 0.0002 | - |
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| 0.6876 | 24050 | 0.0001 | - |
| 0.6891 | 24100 | 0.0002 | - |
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| 0.6919 | 24200 | 0.0003 | - |
| 0.6934 | 24250 | 0.0002 | - |
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| 0.6991 | 24450 | 0.0001 | - |
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| 0.7105 | 24850 | 0.0002 | - |
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| 0.7134 | 24950 | 0.0001 | - |
| 0.7148 | 25000 | 0.0002 | - |
| 0.7162 | 25050 | 0.0001 | - |
| 0.7177 | 25100 | 0.0002 | - |
| 0.7191 | 25150 | 0.0001 | - |
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| 0.7219 | 25250 | 0.0002 | - |
| 0.7234 | 25300 | 0.0002 | - |
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| 0.7262 | 25400 | 0.0001 | - |
| 0.7277 | 25450 | 0.0002 | - |
| 0.7291 | 25500 | 0.0002 | - |
| 0.7305 | 25550 | 0.0002 | - |
| 0.7320 | 25600 | 0.0001 | - |
| 0.7334 | 25650 | 0.0002 | - |
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| 0.7362 | 25750 | 0.0002 | - |
| 0.7377 | 25800 | 0.0002 | - |
| 0.7391 | 25850 | 0.0001 | - |
| 0.7405 | 25900 | 0.0002 | - |
| 0.7420 | 25950 | 0.0002 | - |
| 0.7434 | 26000 | 0.0001 | - |
| 0.7448 | 26050 | 0.0001 | - |
| 0.7462 | 26100 | 0.0001 | - |
| 0.7477 | 26150 | 0.0001 | - |
| 0.7491 | 26200 | 0.0001 | - |
| 0.7505 | 26250 | 0.0002 | - |
| 0.7520 | 26300 | 0.0001 | - |
| 0.7534 | 26350 | 0.0001 | - |
| 0.7548 | 26400 | 0.0001 | - |
| 0.7563 | 26450 | 0.0002 | - |
| 0.7577 | 26500 | 0.0001 | - |
| 0.7591 | 26550 | 0.0002 | - |
| 0.7605 | 26600 | 0.0003 | - |
| 0.7620 | 26650 | 0.0002 | - |
| 0.7634 | 26700 | 0.0002 | - |
| 0.7648 | 26750 | 0.0001 | - |
| 0.7663 | 26800 | 0.0001 | - |
| 0.7677 | 26850 | 0.0002 | - |
| 0.7691 | 26900 | 0.0002 | - |
| 0.7706 | 26950 | 0.0001 | - |
| 0.7720 | 27000 | 0.0001 | - |
| 0.7734 | 27050 | 0.0001 | - |
| 0.7748 | 27100 | 0.0001 | - |
| 0.7763 | 27150 | 0.0001 | - |
| 0.7777 | 27200 | 0.0002 | - |
| 0.7791 | 27250 | 0.0001 | - |
| 0.7806 | 27300 | 0.0001 | - |
| 0.7820 | 27350 | 0.0001 | - |
| 0.7834 | 27400 | 0.0002 | - |
| 0.7848 | 27450 | 0.0001 | - |
| 0.7863 | 27500 | 0.0001 | - |
| 0.7877 | 27550 | 0.0001 | - |
| 0.7891 | 27600 | 0.0001 | - |
| 0.7906 | 27650 | 0.0001 | - |
| 0.7920 | 27700 | 0.0001 | - |
| 0.7934 | 27750 | 0.0001 | - |
| 0.7949 | 27800 | 0.0001 | - |
| 0.7963 | 27850 | 0.0001 | - |
| 0.7977 | 27900 | 0.0001 | - |
| 0.7991 | 27950 | 0.0003 | - |
| 0.8006 | 28000 | 0.0001 | - |
| 0.8020 | 28050 | 0.0002 | - |
| 0.8034 | 28100 | 0.0001 | - |
| 0.8049 | 28150 | 0.0002 | - |
| 0.8063 | 28200 | 0.0 | - |
| 0.8077 | 28250 | 0.0001 | - |
| 0.8091 | 28300 | 0.0001 | - |
| 0.8106 | 28350 | 0.0001 | - |
| 0.8120 | 28400 | 0.0001 | - |
| 0.8134 | 28450 | 0.0002 | - |
| 0.8149 | 28500 | 0.0001 | - |
| 0.8163 | 28550 | 0.0001 | - |
| 0.8177 | 28600 | 0.0001 | - |
| 0.8192 | 28650 | 0.0001 | - |
| 0.8206 | 28700 | 0.0001 | - |
| 0.8220 | 28750 | 0.0002 | - |
| 0.8234 | 28800 | 0.0002 | - |
| 0.8249 | 28850 | 0.0002 | - |
| 0.8263 | 28900 | 0.0001 | - |
| 0.8277 | 28950 | 0.0002 | - |
| 0.8292 | 29000 | 0.0001 | - |
| 0.8306 | 29050 | 0.0002 | - |
| 0.8320 | 29100 | 0.0001 | - |
| 0.8335 | 29150 | 0.0001 | - |
| 0.8349 | 29200 | 0.0001 | - |
| 0.8363 | 29250 | 0.0001 | - |
| 0.8377 | 29300 | 0.0001 | - |
| 0.8392 | 29350 | 0.0001 | - |
| 0.8406 | 29400 | 0.0001 | - |
| 0.8420 | 29450 | 0.0002 | - |
| 0.8435 | 29500 | 0.0001 | - |
| 0.8449 | 29550 | 0.0001 | - |
| 0.8463 | 29600 | 0.0001 | - |
| 0.8477 | 29650 | 0.0001 | - |
| 0.8492 | 29700 | 0.0001 | - |
| 0.8506 | 29750 | 0.0002 | - |
| 0.8520 | 29800 | 0.0002 | - |
| 0.8535 | 29850 | 0.0001 | - |
| 0.8549 | 29900 | 0.0002 | - |
| 0.8563 | 29950 | 0.0002 | - |
| 0.8578 | 30000 | 0.0002 | - |
| 0.8592 | 30050 | 0.0001 | - |
| 0.8606 | 30100 | 0.0002 | - |
| 0.8620 | 30150 | 0.0002 | - |
| 0.8635 | 30200 | 0.0003 | - |
| 0.8649 | 30250 | 0.0001 | - |
| 0.8663 | 30300 | 0.0001 | - |
| 0.8678 | 30350 | 0.0001 | - |
| 0.8692 | 30400 | 0.0001 | - |
| 0.8706 | 30450 | 0.0002 | - |
| 0.8721 | 30500 | 0.0001 | - |
| 0.8735 | 30550 | 0.0001 | - |
| 0.8749 | 30600 | 0.0001 | - |
| 0.8763 | 30650 | 0.0002 | - |
| 0.8778 | 30700 | 0.0002 | - |
| 0.8792 | 30750 | 0.0001 | - |
| 0.8806 | 30800 | 0.0002 | - |
| 0.8821 | 30850 | 0.0002 | - |
| 0.8835 | 30900 | 0.0001 | - |
| 0.8849 | 30950 | 0.0002 | - |
| 0.8863 | 31000 | 0.0002 | - |
| 0.8878 | 31050 | 0.0002 | - |
| 0.8892 | 31100 | 0.0001 | - |
| 0.8906 | 31150 | 0.0001 | - |
| 0.8921 | 31200 | 0.0001 | - |
| 0.8935 | 31250 | 0.0001 | - |
| 0.8949 | 31300 | 0.0002 | - |
| 0.8964 | 31350 | 0.0002 | - |
| 0.8978 | 31400 | 0.0001 | - |
| 0.8992 | 31450 | 0.0001 | - |
| 0.9006 | 31500 | 0.0002 | - |
| 0.9021 | 31550 | 0.0002 | - |
| 0.9035 | 31600 | 0.0001 | - |
| 0.9049 | 31650 | 0.0002 | - |
| 0.9064 | 31700 | 0.0001 | - |
| 0.9078 | 31750 | 0.0001 | - |
| 0.9092 | 31800 | 0.0001 | - |
| 0.9107 | 31850 | 0.0002 | - |
| 0.9121 | 31900 | 0.0002 | - |
| 0.9135 | 31950 | 0.0001 | - |
| 0.9149 | 32000 | 0.0001 | - |
| 0.9164 | 32050 | 0.0001 | - |
| 0.9178 | 32100 | 0.0001 | - |
| 0.9192 | 32150 | 0.0001 | - |
| 0.9207 | 32200 | 0.0001 | - |
| 0.9221 | 32250 | 0.0001 | - |
| 0.9235 | 32300 | 0.0002 | - |
| 0.9249 | 32350 | 0.0001 | - |
| 0.9264 | 32400 | 0.0001 | - |
| 0.9278 | 32450 | 0.0002 | - |
| 0.9292 | 32500 | 0.0001 | - |
| 0.9307 | 32550 | 0.0001 | - |
| 0.9321 | 32600 | 0.0002 | - |
| 0.9335 | 32650 | 0.0001 | - |
| 0.9350 | 32700 | 0.0001 | - |
| 0.9364 | 32750 | 0.0001 | - |
| 0.9378 | 32800 | 0.0001 | - |
| 0.9392 | 32850 | 0.0001 | - |
| 0.9407 | 32900 | 0.0002 | - |
| 0.9421 | 32950 | 0.0002 | - |
| 0.9435 | 33000 | 0.0 | - |
| 0.9450 | 33050 | 0.0001 | - |
| 0.9464 | 33100 | 0.0001 | - |
| 0.9478 | 33150 | 0.0001 | - |
| 0.9492 | 33200 | 0.0001 | - |
| 0.9507 | 33250 | 0.0001 | - |
| 0.9521 | 33300 | 0.0001 | - |
| 0.9535 | 33350 | 0.0002 | - |
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| 0.9593 | 33550 | 0.0001 | - |
| 0.9607 | 33600 | 0.0001 | - |
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| 0.9635 | 33700 | 0.0002 | - |
| 0.9650 | 33750 | 0.0001 | - |
| 0.9664 | 33800 | 0.0001 | - |
| 0.9678 | 33850 | 0.0001 | - |
| 0.9693 | 33900 | 0.0001 | - |
| 0.9707 | 33950 | 0.0 | - |
| 0.9721 | 34000 | 0.0002 | - |
| 0.9736 | 34050 | 0.0001 | - |
| 0.9750 | 34100 | 0.0001 | - |
| 0.9764 | 34150 | 0.0001 | - |
| 0.9778 | 34200 | 0.0001 | - |
| 0.9793 | 34250 | 0.0002 | - |
| 0.9807 | 34300 | 0.0002 | - |
| 0.9821 | 34350 | 0.0001 | - |
| 0.9836 | 34400 | 0.0001 | - |
| 0.9850 | 34450 | 0.0001 | - |
| 0.9864 | 34500 | 0.0001 | - |
| 0.9878 | 34550 | 0.0001 | - |
| 0.9893 | 34600 | 0.0001 | - |
| 0.9907 | 34650 | 0.0001 | - |
| 0.9921 | 34700 | 0.0001 | - |
| 0.9936 | 34750 | 0.0001 | - |
| 0.9950 | 34800 | 0.0001 | - |
| 0.9964 | 34850 | 0.0001 | - |
| 0.9979 | 34900 | 0.0002 | - |
| 0.9993 | 34950 | 0.0002 | - |
| **1.0** | **34975** | **-** | **0.0221** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.9
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- 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}
}
```
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