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---
base_model: BAAI/bge-large-en-v1.5
datasets:
- nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Can you filter by the 'Fashion' category and show me the products available?
- text: Get forecast by service type.
- text: How many orders were placed in each quarter?
- text: What are the details of customers with no phone number listed?
- text: I don't want to filter the database currently.
inference: true
model-index:
- name: SetFit with BAAI/bge-large-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
type: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
split: test
metrics:
- type: accuracy
value: 0.9739130434782609
name: Accuracy
---
# SetFit with BAAI/bge-large-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed](https://huggingface.co./datasets/nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed) dataset that can be used for Text Classification. This SetFit model uses [BAAI/bge-large-en-v1.5](https://huggingface.co./BAAI/bge-large-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-large-en-v1.5](https://huggingface.co./BAAI/bge-large-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:** [nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed](https://huggingface.co./datasets/nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed)
<!-- - **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 |
|:-------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Aggregation | <ul><li>'How many unique customers made purchases last year?'</li><li>'Determine the minimum order amount for each customer.'</li><li>'Get me sum of total_revenue.'</li></ul> |
| Tablejoin | <ul><li>'Show me a join of cash flow and variance.'</li><li>'Join data_asset_001_forecast with data_asset_kpi_bs tables.'</li><li>'Join data_asset_kpi_ma_product with data_asset_001_variance.'</li></ul> |
| Lookup_1 | <ul><li>'Show me asset impairment by year.'</li><li>'Get me data_asset_001_pcc group by category.'</li><li>'Show me data_asset_001_variance group by category.'</li></ul> |
| Viewtables | <ul><li>'What are the table names within the starhub_data_asset database that enable data analysis of customer feedback?'</li><li>'How can I access the table directory for starhub_data_asset database to view all the available tables?'</li><li>'Please show me the tables that contain data related to customer transactions present in the starhub_data_asset database.'</li></ul> |
| Generalreply | <ul><li>"Oh my favorite food? That's a tough one. I love so many different kinds of food, but if I had to choose one it would probably be pizza. What about you? What's your favorite food?"</li><li>"Hmm, let me think... I'm actually pretty good at playing guitar! I've been playing for a few years now and it's always been one of my favorite hobbies. How about you, do you play any instruments or have any interesting hobbies?"</li><li>'What is your favorite color?'</li></ul> |
| Lookup | <ul><li>"Get me all the customers who haven't placed any orders."</li><li>'Get me the list of customers who have a phone number listed.'</li><li>'Can you filter by customers who registered without an email address?'</li></ul> |
| Rejection | <ul><li>"I'm not keen on producing any new data sets."</li><li>"Please don't generate any new data."</li><li>"I don't want to create any new data outputs."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9739 |
## 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-large-en-v1.5-brahmaputra-iter-8-2nd-1-epoch")
# Run inference
preds = model("Get forecast by service type.")
```
<!--
### 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 | 2 | 8.8252 | 62 |
| Label | Training Sample Count |
|:-------------|:----------------------|
| Tablejoin | 129 |
| Rejection | 74 |
| Aggregation | 210 |
| Lookup | 60 |
| Generalreply | 59 |
| Viewtables | 75 |
| Lookup_1 | 217 |
### 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 | 0.1706 | - |
| 0.0014 | 50 | 0.1976 | - |
| 0.0029 | 100 | 0.2045 | - |
| 0.0043 | 150 | 0.1846 | - |
| 0.0058 | 200 | 0.1608 | - |
| 0.0072 | 250 | 0.105 | - |
| 0.0087 | 300 | 0.1618 | - |
| 0.0101 | 350 | 0.1282 | - |
| 0.0116 | 400 | 0.0382 | - |
| 0.0130 | 450 | 0.0328 | - |
| 0.0145 | 500 | 0.0483 | - |
| 0.0159 | 550 | 0.0245 | - |
| 0.0174 | 600 | 0.0093 | - |
| 0.0188 | 650 | 0.0084 | - |
| 0.0203 | 700 | 0.0042 | - |
| 0.0217 | 750 | 0.0044 | - |
| 0.0231 | 800 | 0.0035 | - |
| 0.0246 | 850 | 0.0065 | - |
| 0.0260 | 900 | 0.0036 | - |
| 0.0275 | 950 | 0.0039 | - |
| 0.0289 | 1000 | 0.0037 | - |
| 0.0304 | 1050 | 0.005 | - |
| 0.0318 | 1100 | 0.0024 | - |
| 0.0333 | 1150 | 0.0023 | - |
| 0.0347 | 1200 | 0.0023 | - |
| 0.0362 | 1250 | 0.0019 | - |
| 0.0376 | 1300 | 0.0015 | - |
| 0.0391 | 1350 | 0.0023 | - |
| 0.0405 | 1400 | 0.0011 | - |
| 0.0420 | 1450 | 0.0017 | - |
| 0.0434 | 1500 | 0.0015 | - |
| 0.0448 | 1550 | 0.0014 | - |
| 0.0463 | 1600 | 0.0014 | - |
| 0.0477 | 1650 | 0.0013 | - |
| 0.0492 | 1700 | 0.0013 | - |
| 0.0506 | 1750 | 0.001 | - |
| 0.0521 | 1800 | 0.0013 | - |
| 0.0535 | 1850 | 0.0013 | - |
| 0.0550 | 1900 | 0.0011 | - |
| 0.0564 | 1950 | 0.0012 | - |
| 0.0579 | 2000 | 0.001 | - |
| 0.0593 | 2050 | 0.0012 | - |
| 0.0608 | 2100 | 0.0008 | - |
| 0.0622 | 2150 | 0.0008 | - |
| 0.0637 | 2200 | 0.001 | - |
| 0.0651 | 2250 | 0.0007 | - |
| 0.0665 | 2300 | 0.0006 | - |
| 0.0680 | 2350 | 0.0007 | - |
| 0.0694 | 2400 | 0.0008 | - |
| 0.0709 | 2450 | 0.0008 | - |
| 0.0723 | 2500 | 0.0006 | - |
| 0.0738 | 2550 | 0.0006 | - |
| 0.0752 | 2600 | 0.0007 | - |
| 0.0767 | 2650 | 0.0008 | - |
| 0.0781 | 2700 | 0.0005 | - |
| 0.0796 | 2750 | 0.0008 | - |
| 0.0810 | 2800 | 0.0006 | - |
| 0.0825 | 2850 | 0.0007 | - |
| 0.0839 | 2900 | 0.0007 | - |
| 0.0854 | 2950 | 0.0005 | - |
| 0.0868 | 3000 | 0.0007 | - |
| 0.0882 | 3050 | 0.0005 | - |
| 0.0897 | 3100 | 0.0005 | - |
| 0.0911 | 3150 | 0.0007 | - |
| 0.0926 | 3200 | 0.0005 | - |
| 0.0940 | 3250 | 0.0005 | - |
| 0.0955 | 3300 | 0.0007 | - |
| 0.0969 | 3350 | 0.0004 | - |
| 0.0984 | 3400 | 0.0005 | - |
| 0.0998 | 3450 | 0.0004 | - |
| 0.1013 | 3500 | 0.0007 | - |
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| 0.1085 | 3750 | 0.0004 | - |
| 0.1100 | 3800 | 0.0005 | - |
| 0.1114 | 3850 | 0.0004 | - |
| 0.1128 | 3900 | 0.0004 | - |
| 0.1143 | 3950 | 0.0003 | - |
| 0.1157 | 4000 | 0.0004 | - |
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| 0.1186 | 4100 | 0.0004 | - |
| 0.1201 | 4150 | 0.0004 | - |
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| 0.1722 | 5950 | 0.0007 | - |
| 0.1736 | 6000 | 0.0003 | - |
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| 0.1968 | 6800 | 0.0002 | - |
| 0.1982 | 6850 | 0.0003 | - |
| 0.1996 | 6900 | 0.0003 | - |
| 0.2011 | 6950 | 0.0002 | - |
| 0.2025 | 7000 | 0.0002 | - |
| 0.2040 | 7050 | 0.0001 | - |
| 0.2054 | 7100 | 0.0002 | - |
| 0.2069 | 7150 | 0.0002 | - |
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| 0.2098 | 7250 | 0.0002 | - |
| 0.2112 | 7300 | 0.0002 | - |
| 0.2127 | 7350 | 0.0002 | - |
| 0.2141 | 7400 | 0.0002 | - |
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| 0.2170 | 7500 | 0.0002 | - |
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| 0.2199 | 7600 | 0.0003 | - |
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| 0.2300 | 7950 | 0.0002 | - |
| 0.2315 | 8000 | 0.0001 | - |
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| 0.2344 | 8100 | 0.0002 | - |
| 0.2358 | 8150 | 0.0002 | - |
| 0.2373 | 8200 | 0.0002 | - |
| 0.2387 | 8250 | 0.0002 | - |
| 0.2402 | 8300 | 0.0001 | - |
| 0.2416 | 8350 | 0.0005 | - |
| 0.2430 | 8400 | 0.002 | - |
| 0.2445 | 8450 | 0.0037 | - |
| 0.2459 | 8500 | 0.0516 | - |
| 0.2474 | 8550 | 0.0028 | - |
| 0.2488 | 8600 | 0.0013 | - |
| 0.2503 | 8650 | 0.0017 | - |
| 0.2517 | 8700 | 0.0012 | - |
| 0.2532 | 8750 | 0.0513 | - |
| 0.2546 | 8800 | 0.001 | - |
| 0.2561 | 8850 | 0.035 | - |
| 0.2575 | 8900 | 0.0005 | - |
| 0.2590 | 8950 | 0.0076 | - |
| 0.2604 | 9000 | 0.0113 | - |
| 0.2619 | 9050 | 0.0006 | - |
| 0.2633 | 9100 | 0.0006 | - |
| 0.2647 | 9150 | 0.0018 | - |
| 0.2662 | 9200 | 0.0025 | - |
| 0.2676 | 9250 | 0.0011 | - |
| 0.2691 | 9300 | 0.001 | - |
| 0.2705 | 9350 | 0.0011 | - |
| 0.2720 | 9400 | 0.0004 | - |
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| 0.2879 | 9950 | 0.0007 | - |
| 0.2893 | 10000 | 0.0014 | - |
| 0.2908 | 10050 | 0.0007 | - |
| 0.2922 | 10100 | 0.0003 | - |
| 0.2937 | 10150 | 0.0015 | - |
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| 0.2995 | 10350 | 0.0003 | - |
| 0.3009 | 10400 | 0.0004 | - |
| 0.3024 | 10450 | 0.0003 | - |
| 0.3038 | 10500 | 0.0008 | - |
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| 0.3067 | 10600 | 0.0005 | - |
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| 0.3096 | 10700 | 0.0006 | - |
| 0.3110 | 10750 | 0.0002 | - |
| 0.3125 | 10800 | 0.0008 | - |
| 0.3139 | 10850 | 0.0005 | - |
| 0.3154 | 10900 | 0.0004 | - |
| 0.3168 | 10950 | 0.0002 | - |
| 0.3183 | 11000 | 0.0002 | - |
| 0.3197 | 11050 | 0.0002 | - |
| 0.3212 | 11100 | 0.0006 | - |
| 0.3226 | 11150 | 0.0003 | - |
| 0.3241 | 11200 | 0.0002 | - |
| 0.3255 | 11250 | 0.0002 | - |
| 0.3270 | 11300 | 0.0003 | - |
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| 0.3414 | 11800 | 0.0003 | - |
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| 0.3472 | 12000 | 0.0004 | - |
| 0.3487 | 12050 | 0.0002 | - |
| 0.3501 | 12100 | 0.0002 | - |
| 0.3516 | 12150 | 0.0002 | - |
| 0.3530 | 12200 | 0.0002 | - |
| 0.3544 | 12250 | 0.0002 | - |
| 0.3559 | 12300 | 0.0002 | - |
| 0.3573 | 12350 | 0.0003 | - |
| 0.3588 | 12400 | 0.0002 | - |
| 0.3602 | 12450 | 0.0002 | - |
| 0.3617 | 12500 | 0.0002 | - |
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| 0.3646 | 12600 | 0.0003 | - |
| 0.3660 | 12650 | 0.0003 | - |
| 0.3675 | 12700 | 0.0002 | - |
| 0.3689 | 12750 | 0.0004 | - |
| 0.3704 | 12800 | 0.0003 | - |
| 0.3718 | 12850 | 0.0003 | - |
| 0.3733 | 12900 | 0.0001 | - |
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| 0.3761 | 13000 | 0.0001 | - |
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| 0.3790 | 13100 | 0.0001 | - |
| 0.3805 | 13150 | 0.0001 | - |
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| 0.3834 | 13250 | 0.0003 | - |
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| 0.4080 | 14100 | 0.0002 | - |
| 0.4094 | 14150 | 0.0002 | - |
| 0.4109 | 14200 | 0.0002 | - |
| 0.4123 | 14250 | 0.0002 | - |
| 0.4138 | 14300 | 0.0002 | - |
| 0.4152 | 14350 | 0.0002 | - |
| 0.4167 | 14400 | 0.0002 | - |
| 0.4181 | 14450 | 0.0003 | - |
| 0.4195 | 14500 | 0.0002 | - |
| 0.4210 | 14550 | 0.0002 | - |
| 0.4224 | 14600 | 0.0001 | - |
| 0.4239 | 14650 | 0.0003 | - |
| 0.4253 | 14700 | 0.0002 | - |
| 0.4268 | 14750 | 0.0002 | - |
| 0.4282 | 14800 | 0.0002 | - |
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| 0.4326 | 14950 | 0.0003 | - |
| 0.4340 | 15000 | 0.0002 | - |
| 0.4355 | 15050 | 0.0002 | - |
| 0.4369 | 15100 | 0.0002 | - |
| 0.4384 | 15150 | 0.0002 | - |
| 0.4398 | 15200 | 0.0002 | - |
| 0.4412 | 15250 | 0.0001 | - |
| 0.4427 | 15300 | 0.0002 | - |
| 0.4441 | 15350 | 0.0003 | - |
| 0.4456 | 15400 | 0.0003 | - |
| 0.4470 | 15450 | 0.0003 | - |
| 0.4485 | 15500 | 0.0002 | - |
| 0.4499 | 15550 | 0.0001 | - |
| 0.4514 | 15600 | 0.0001 | - |
| 0.4528 | 15650 | 0.0001 | - |
| 0.4543 | 15700 | 0.0001 | - |
| 0.4557 | 15750 | 0.0002 | - |
| 0.4572 | 15800 | 0.0001 | - |
| 0.4586 | 15850 | 0.0002 | - |
| 0.4601 | 15900 | 0.0003 | - |
| 0.4615 | 15950 | 0.0002 | - |
| 0.4629 | 16000 | 0.0002 | - |
| 0.4644 | 16050 | 0.0002 | - |
| 0.4658 | 16100 | 0.0001 | - |
| 0.4673 | 16150 | 0.0001 | - |
| 0.4687 | 16200 | 0.0001 | - |
| 0.4702 | 16250 | 0.0002 | - |
| 0.4716 | 16300 | 0.0003 | - |
| 0.4731 | 16350 | 0.0001 | - |
| 0.4745 | 16400 | 0.0001 | - |
| 0.4760 | 16450 | 0.0001 | - |
| 0.4774 | 16500 | 0.0002 | - |
| 0.4789 | 16550 | 0.0006 | - |
| 0.4803 | 16600 | 0.0002 | - |
| 0.4818 | 16650 | 0.0001 | - |
| 0.4832 | 16700 | 0.0002 | - |
| 0.4847 | 16750 | 0.0001 | - |
| 0.4861 | 16800 | 0.0003 | - |
| 0.4875 | 16850 | 0.0001 | - |
| 0.4890 | 16900 | 0.0002 | - |
| 0.4904 | 16950 | 0.0002 | - |
| 0.4919 | 17000 | 0.0001 | - |
| 0.4933 | 17050 | 0.0002 | - |
| 0.4948 | 17100 | 0.0001 | - |
| 0.4962 | 17150 | 0.0002 | - |
| 0.4977 | 17200 | 0.0002 | - |
| 0.4991 | 17250 | 0.0001 | - |
| 0.5006 | 17300 | 0.0002 | - |
| 0.5020 | 17350 | 0.0002 | - |
| 0.5035 | 17400 | 0.0001 | - |
| 0.5049 | 17450 | 0.0002 | - |
| 0.5064 | 17500 | 0.0003 | - |
| 0.5078 | 17550 | 0.0001 | - |
| 0.5092 | 17600 | 0.0002 | - |
| 0.5107 | 17650 | 0.0001 | - |
| 0.5121 | 17700 | 0.0002 | - |
| 0.5136 | 17750 | 0.0002 | - |
| 0.5150 | 17800 | 0.0003 | - |
| 0.5165 | 17850 | 0.0002 | - |
| 0.5179 | 17900 | 0.0002 | - |
| 0.5194 | 17950 | 0.0001 | - |
| 0.5208 | 18000 | 0.0002 | - |
| 0.5223 | 18050 | 0.0001 | - |
| 0.5237 | 18100 | 0.0001 | - |
| 0.5252 | 18150 | 0.0001 | - |
| 0.5266 | 18200 | 0.0003 | - |
| 0.5281 | 18250 | 0.0001 | - |
| 0.5295 | 18300 | 0.0001 | - |
| 0.5309 | 18350 | 0.0001 | - |
| 0.5324 | 18400 | 0.0001 | - |
| 0.5338 | 18450 | 0.0002 | - |
| 0.5353 | 18500 | 0.0008 | - |
| 0.5367 | 18550 | 0.0002 | - |
| 0.5382 | 18600 | 0.0001 | - |
| 0.5396 | 18650 | 0.0002 | - |
| 0.5411 | 18700 | 0.0002 | - |
| 0.5425 | 18750 | 0.0001 | - |
| 0.5440 | 18800 | 0.0001 | - |
| 0.5454 | 18850 | 0.0001 | - |
| 0.5469 | 18900 | 0.0002 | - |
| 0.5483 | 18950 | 0.0001 | - |
| 0.5498 | 19000 | 0.0001 | - |
| 0.5512 | 19050 | 0.0001 | - |
| 0.5526 | 19100 | 0.0002 | - |
| 0.5541 | 19150 | 0.0001 | - |
| 0.5555 | 19200 | 0.0001 | - |
| 0.5570 | 19250 | 0.0002 | - |
| 0.5584 | 19300 | 0.0001 | - |
| 0.5599 | 19350 | 0.0002 | - |
| 0.5613 | 19400 | 0.0001 | - |
| 0.5628 | 19450 | 0.0002 | - |
| 0.5642 | 19500 | 0.0001 | - |
| 0.5657 | 19550 | 0.0002 | - |
| 0.5671 | 19600 | 0.0002 | - |
| 0.5686 | 19650 | 0.0002 | - |
| 0.5700 | 19700 | 0.0001 | - |
| 0.5715 | 19750 | 0.0001 | - |
| 0.5729 | 19800 | 0.0003 | - |
| 0.5743 | 19850 | 0.0001 | - |
| 0.5758 | 19900 | 0.0001 | - |
| 0.5772 | 19950 | 0.0001 | - |
| 0.5787 | 20000 | 0.0001 | - |
| 0.5801 | 20050 | 0.0001 | - |
| 0.5816 | 20100 | 0.0001 | - |
| 0.5830 | 20150 | 0.0001 | - |
| 0.5845 | 20200 | 0.0001 | - |
| 0.5859 | 20250 | 0.0001 | - |
| 0.5874 | 20300 | 0.0002 | - |
| 0.5888 | 20350 | 0.0002 | - |
| 0.5903 | 20400 | 0.0001 | - |
| 0.5917 | 20450 | 0.0002 | - |
| 0.5932 | 20500 | 0.0001 | - |
| 0.5946 | 20550 | 0.0001 | - |
| 0.5960 | 20600 | 0.0001 | - |
| 0.5975 | 20650 | 0.0002 | - |
| 0.5989 | 20700 | 0.0002 | - |
| 0.6004 | 20750 | 0.0001 | - |
| 0.6018 | 20800 | 0.0001 | - |
| 0.6033 | 20850 | 0.0002 | - |
| 0.6047 | 20900 | 0.0001 | - |
| 0.6062 | 20950 | 0.0002 | - |
| 0.6076 | 21000 | 0.0001 | - |
| 0.6091 | 21050 | 0.0001 | - |
| 0.6105 | 21100 | 0.0001 | - |
| 0.6120 | 21150 | 0.0002 | - |
| 0.6134 | 21200 | 0.0001 | - |
| 0.6149 | 21250 | 0.0001 | - |
| 0.6163 | 21300 | 0.0001 | - |
| 0.6177 | 21350 | 0.0001 | - |
| 0.6192 | 21400 | 0.0002 | - |
| 0.6206 | 21450 | 0.0001 | - |
| 0.6221 | 21500 | 0.0002 | - |
| 0.6235 | 21550 | 0.0003 | - |
| 0.6250 | 21600 | 0.0001 | - |
| 0.6264 | 21650 | 0.0001 | - |
| 0.6279 | 21700 | 0.0001 | - |
| 0.6293 | 21750 | 0.0001 | - |
| 0.6308 | 21800 | 0.0002 | - |
| 0.6322 | 21850 | 0.0001 | - |
| 0.6337 | 21900 | 0.0001 | - |
| 0.6351 | 21950 | 0.0001 | - |
| 0.6366 | 22000 | 0.0002 | - |
| 0.6380 | 22050 | 0.0001 | - |
| 0.6394 | 22100 | 0.0001 | - |
| 0.6409 | 22150 | 0.0002 | - |
| 0.6423 | 22200 | 0.0002 | - |
| 0.6438 | 22250 | 0.0003 | - |
| 0.6452 | 22300 | 0.0001 | - |
| 0.6467 | 22350 | 0.0001 | - |
| 0.6481 | 22400 | 0.0001 | - |
| 0.6496 | 22450 | 0.0002 | - |
| 0.6510 | 22500 | 0.0001 | - |
| 0.6525 | 22550 | 0.0001 | - |
| 0.6539 | 22600 | 0.0001 | - |
| 0.6554 | 22650 | 0.0001 | - |
| 0.6568 | 22700 | 0.0002 | - |
| 0.6583 | 22750 | 0.0001 | - |
| 0.6597 | 22800 | 0.0001 | - |
| 0.6611 | 22850 | 0.0001 | - |
| 0.6626 | 22900 | 0.0001 | - |
| 0.6640 | 22950 | 0.0001 | - |
| 0.6655 | 23000 | 0.0001 | - |
| 0.6669 | 23050 | 0.0002 | - |
| 0.6684 | 23100 | 0.0001 | - |
| 0.6698 | 23150 | 0.0001 | - |
| 0.6713 | 23200 | 0.0001 | - |
| 0.6727 | 23250 | 0.0001 | - |
| 0.6742 | 23300 | 0.0002 | - |
| 0.6756 | 23350 | 0.0002 | - |
| 0.6771 | 23400 | 0.0001 | - |
| 0.6785 | 23450 | 0.0001 | - |
| 0.6800 | 23500 | 0.0001 | - |
| 0.6814 | 23550 | 0.0001 | - |
| 0.6829 | 23600 | 0.0002 | - |
| 0.6843 | 23650 | 0.0001 | - |
| 0.6857 | 23700 | 0.0001 | - |
| 0.6872 | 23750 | 0.0001 | - |
| 0.6886 | 23800 | 0.0001 | - |
| 0.6901 | 23850 | 0.0002 | - |
| 0.6915 | 23900 | 0.0001 | - |
| 0.6930 | 23950 | 0.0001 | - |
| 0.6944 | 24000 | 0.0002 | - |
| 0.6959 | 24050 | 0.0001 | - |
| 0.6973 | 24100 | 0.0001 | - |
| 0.6988 | 24150 | 0.0001 | - |
| 0.7002 | 24200 | 0.0001 | - |
| 0.7017 | 24250 | 0.0001 | - |
| 0.7031 | 24300 | 0.0001 | - |
| 0.7046 | 24350 | 0.0001 | - |
| 0.7060 | 24400 | 0.0001 | - |
| 0.7074 | 24450 | 0.0002 | - |
| 0.7089 | 24500 | 0.0001 | - |
| 0.7103 | 24550 | 0.0002 | - |
| 0.7118 | 24600 | 0.0001 | - |
| 0.7132 | 24650 | 0.0001 | - |
| 0.7147 | 24700 | 0.0001 | - |
| 0.7161 | 24750 | 0.0001 | - |
| 0.7176 | 24800 | 0.0001 | - |
| 0.7190 | 24850 | 0.0001 | - |
| 0.7205 | 24900 | 0.0001 | - |
| 0.7219 | 24950 | 0.0001 | - |
| 0.7234 | 25000 | 0.0001 | - |
| 0.7248 | 25050 | 0.0002 | - |
| 0.7263 | 25100 | 0.0001 | - |
| 0.7277 | 25150 | 0.0001 | - |
| 0.7291 | 25200 | 0.0001 | - |
| 0.7306 | 25250 | 0.0001 | - |
| 0.7320 | 25300 | 0.0001 | - |
| 0.7335 | 25350 | 0.0001 | - |
| 0.7349 | 25400 | 0.0 | - |
| 0.7364 | 25450 | 0.0001 | - |
| 0.7378 | 25500 | 0.0001 | - |
| 0.7393 | 25550 | 0.0001 | - |
| 0.7407 | 25600 | 0.0001 | - |
| 0.7422 | 25650 | 0.0001 | - |
| 0.7436 | 25700 | 0.0001 | - |
| 0.7451 | 25750 | 0.0001 | - |
| 0.7465 | 25800 | 0.0 | - |
| 0.7480 | 25850 | 0.0001 | - |
| 0.7494 | 25900 | 0.0001 | - |
| 0.7508 | 25950 | 0.0001 | - |
| 0.7523 | 26000 | 0.0001 | - |
| 0.7537 | 26050 | 0.0001 | - |
| 0.7552 | 26100 | 0.0001 | - |
| 0.7566 | 26150 | 0.0001 | - |
| 0.7581 | 26200 | 0.0001 | - |
| 0.7595 | 26250 | 0.0001 | - |
| 0.7610 | 26300 | 0.0001 | - |
| 0.7624 | 26350 | 0.0001 | - |
| 0.7639 | 26400 | 0.0002 | - |
| 0.7653 | 26450 | 0.0001 | - |
| 0.7668 | 26500 | 0.0001 | - |
| 0.7682 | 26550 | 0.0001 | - |
| 0.7697 | 26600 | 0.0001 | - |
| 0.7711 | 26650 | 0.0002 | - |
| 0.7725 | 26700 | 0.0001 | - |
| 0.7740 | 26750 | 0.0001 | - |
| 0.7754 | 26800 | 0.0001 | - |
| 0.7769 | 26850 | 0.0001 | - |
| 0.7783 | 26900 | 0.0001 | - |
| 0.7798 | 26950 | 0.0001 | - |
| 0.7812 | 27000 | 0.0001 | - |
| 0.7827 | 27050 | 0.0001 | - |
| 0.7841 | 27100 | 0.0001 | - |
| 0.7856 | 27150 | 0.0001 | - |
| 0.7870 | 27200 | 0.0001 | - |
| 0.7885 | 27250 | 0.0001 | - |
| 0.7899 | 27300 | 0.0001 | - |
| 0.7914 | 27350 | 0.0001 | - |
| 0.7928 | 27400 | 0.0001 | - |
| 0.7942 | 27450 | 0.0001 | - |
| 0.7957 | 27500 | 0.0001 | - |
| 0.7971 | 27550 | 0.0001 | - |
| 0.7986 | 27600 | 0.0001 | - |
| 0.8000 | 27650 | 0.0001 | - |
| 0.8015 | 27700 | 0.0001 | - |
| 0.8029 | 27750 | 0.0001 | - |
| 0.8044 | 27800 | 0.0 | - |
| 0.8058 | 27850 | 0.0001 | - |
| 0.8073 | 27900 | 0.0001 | - |
| 0.8087 | 27950 | 0.0001 | - |
| 0.8102 | 28000 | 0.0001 | - |
| 0.8116 | 28050 | 0.0 | - |
| 0.8131 | 28100 | 0.0 | - |
| 0.8145 | 28150 | 0.0001 | - |
| 0.8159 | 28200 | 0.0001 | - |
| 0.8174 | 28250 | 0.0001 | - |
| 0.8188 | 28300 | 0.0001 | - |
| 0.8203 | 28350 | 0.0001 | - |
| 0.8217 | 28400 | 0.0001 | - |
| 0.8232 | 28450 | 0.0001 | - |
| 0.8246 | 28500 | 0.0001 | - |
| 0.8261 | 28550 | 0.0001 | - |
| 0.8275 | 28600 | 0.0001 | - |
| 0.8290 | 28650 | 0.0001 | - |
| 0.8304 | 28700 | 0.0001 | - |
| 0.8319 | 28750 | 0.0001 | - |
| 0.8333 | 28800 | 0.0001 | - |
| 0.8348 | 28850 | 0.0002 | - |
| 0.8362 | 28900 | 0.0001 | - |
| 0.8376 | 28950 | 0.0001 | - |
| 0.8391 | 29000 | 0.0001 | - |
| 0.8405 | 29050 | 0.0001 | - |
| 0.8420 | 29100 | 0.0001 | - |
| 0.8434 | 29150 | 0.0001 | - |
| 0.8449 | 29200 | 0.0 | - |
| 0.8463 | 29250 | 0.0001 | - |
| 0.8478 | 29300 | 0.0001 | - |
| 0.8492 | 29350 | 0.0001 | - |
| 0.8507 | 29400 | 0.0001 | - |
| 0.8521 | 29450 | 0.0001 | - |
| 0.8536 | 29500 | 0.0001 | - |
| 0.8550 | 29550 | 0.0001 | - |
| 0.8565 | 29600 | 0.0002 | - |
| 0.8579 | 29650 | 0.0 | - |
| 0.8594 | 29700 | 0.0001 | - |
| 0.8608 | 29750 | 0.0001 | - |
| 0.8622 | 29800 | 0.0001 | - |
| 0.8637 | 29850 | 0.0001 | - |
| 0.8651 | 29900 | 0.0 | - |
| 0.8666 | 29950 | 0.0001 | - |
| 0.8680 | 30000 | 0.0001 | - |
| 0.8695 | 30050 | 0.0001 | - |
| 0.8709 | 30100 | 0.0 | - |
| 0.8724 | 30150 | 0.0 | - |
| 0.8738 | 30200 | 0.0001 | - |
| 0.8753 | 30250 | 0.0001 | - |
| 0.8767 | 30300 | 0.0001 | - |
| 0.8782 | 30350 | 0.0001 | - |
| 0.8796 | 30400 | 0.0001 | - |
| 0.8811 | 30450 | 0.0001 | - |
| 0.8825 | 30500 | 0.0001 | - |
| 0.8839 | 30550 | 0.0001 | - |
| 0.8854 | 30600 | 0.0 | - |
| 0.8868 | 30650 | 0.0001 | - |
| 0.8883 | 30700 | 0.0001 | - |
| 0.8897 | 30750 | 0.0001 | - |
| 0.8912 | 30800 | 0.0001 | - |
| 0.8926 | 30850 | 0.0 | - |
| 0.8941 | 30900 | 0.0 | - |
| 0.8955 | 30950 | 0.0001 | - |
| 0.8970 | 31000 | 0.0001 | - |
| 0.8984 | 31050 | 0.0001 | - |
| 0.8999 | 31100 | 0.0001 | - |
| 0.9013 | 31150 | 0.0 | - |
| 0.9028 | 31200 | 0.0001 | - |
| 0.9042 | 31250 | 0.0001 | - |
| 0.9056 | 31300 | 0.0001 | - |
| 0.9071 | 31350 | 0.0001 | - |
| 0.9085 | 31400 | 0.0001 | - |
| 0.9100 | 31450 | 0.0002 | - |
| 0.9114 | 31500 | 0.0001 | - |
| 0.9129 | 31550 | 0.0001 | - |
| 0.9143 | 31600 | 0.0001 | - |
| 0.9158 | 31650 | 0.0001 | - |
| 0.9172 | 31700 | 0.0001 | - |
| 0.9187 | 31750 | 0.0001 | - |
| 0.9201 | 31800 | 0.0001 | - |
| 0.9216 | 31850 | 0.0001 | - |
| 0.9230 | 31900 | 0.0001 | - |
| 0.9245 | 31950 | 0.0001 | - |
| 0.9259 | 32000 | 0.0001 | - |
| 0.9273 | 32050 | 0.0 | - |
| 0.9288 | 32100 | 0.0002 | - |
| 0.9302 | 32150 | 0.0001 | - |
| 0.9317 | 32200 | 0.0001 | - |
| 0.9331 | 32250 | 0.0001 | - |
| 0.9346 | 32300 | 0.0002 | - |
| 0.9360 | 32350 | 0.0 | - |
| 0.9375 | 32400 | 0.0001 | - |
| 0.9389 | 32450 | 0.0001 | - |
| 0.9404 | 32500 | 0.0 | - |
| 0.9418 | 32550 | 0.0001 | - |
| 0.9433 | 32600 | 0.0001 | - |
| 0.9447 | 32650 | 0.0001 | - |
| 0.9462 | 32700 | 0.0001 | - |
| 0.9476 | 32750 | 0.0001 | - |
| 0.9490 | 32800 | 0.0001 | - |
| 0.9505 | 32850 | 0.0001 | - |
| 0.9519 | 32900 | 0.0 | - |
| 0.9534 | 32950 | 0.0001 | - |
| 0.9548 | 33000 | 0.0001 | - |
| 0.9563 | 33050 | 0.0001 | - |
| 0.9577 | 33100 | 0.0001 | - |
| 0.9592 | 33150 | 0.0001 | - |
| 0.9606 | 33200 | 0.0001 | - |
| 0.9621 | 33250 | 0.0001 | - |
| 0.9635 | 33300 | 0.0001 | - |
| 0.9650 | 33350 | 0.0 | - |
| 0.9664 | 33400 | 0.0001 | - |
| 0.9679 | 33450 | 0.0001 | - |
| 0.9693 | 33500 | 0.0 | - |
| 0.9707 | 33550 | 0.0001 | - |
| 0.9722 | 33600 | 0.0 | - |
| 0.9736 | 33650 | 0.0001 | - |
| 0.9751 | 33700 | 0.0001 | - |
| 0.9765 | 33750 | 0.0001 | - |
| 0.9780 | 33800 | 0.0 | - |
| 0.9794 | 33850 | 0.0001 | - |
| 0.9809 | 33900 | 0.0001 | - |
| 0.9823 | 33950 | 0.0001 | - |
| 0.9838 | 34000 | 0.0001 | - |
| 0.9852 | 34050 | 0.0 | - |
| 0.9867 | 34100 | 0.0001 | - |
| 0.9881 | 34150 | 0.0 | - |
| 0.9896 | 34200 | 0.0001 | - |
| 0.9910 | 34250 | 0.0 | - |
| 0.9924 | 34300 | 0.0001 | - |
| 0.9939 | 34350 | 0.0 | - |
| 0.9953 | 34400 | 0.0001 | - |
| 0.9968 | 34450 | 0.0 | - |
| 0.9982 | 34500 | 0.0 | - |
| 0.9997 | 34550 | 0.0001 | - |
| **1.0** | **34561** | **-** | **0.0036** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.9
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- 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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