|
|
--- |
|
|
base_model: sentence-transformers/all-MiniLM-L6-v2 |
|
|
library_name: setfit |
|
|
metrics: |
|
|
- accuracy |
|
|
pipeline_tag: text-classification |
|
|
tags: |
|
|
- setfit |
|
|
- sentence-transformers |
|
|
- text-classification |
|
|
- generated_from_setfit_trainer |
|
|
widget: |
|
|
- text: Thank you for your email. Please go ahead and issue. Please invoice in KES |
|
|
- text: Hi, We are missing some invoices, can you please provide it. 02 - 12 - 2020 |
|
|
AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160 $22.00 02 |
|
|
- 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT FEE 8900783383420 |
|
|
$21.00 |
|
|
- text: We need your assistance with the payment for the recent office supplies order. |
|
|
Let us know once it's done. |
|
|
- text: I have reported this in November and not only was the trip supposed to be |
|
|
cancelled and credited I was double billed and the billing has not been corrected. |
|
|
The total credit should be $667.20. Please confirm this will be done. |
|
|
- text: The invoice for the travel arrangements needs to be settled. Kindly provide |
|
|
payment confirmation. |
|
|
inference: true |
|
|
--- |
|
|
|
|
|
# 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:** 14 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) |
|
|
|
|
|
|
|
|
## 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("mann2107/BCMPIIRAB_MiniLM_ALLNew") |
|
|
# Run inference |
|
|
preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES") |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### 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 | 25.6577 | 136 | |
|
|
|
|
|
| Label | Training Sample Count | |
|
|
|:------|:----------------------| |
|
|
| 0 | 24 | |
|
|
| 1 | 24 | |
|
|
| 2 | 24 | |
|
|
| 3 | 24 | |
|
|
| 4 | 24 | |
|
|
| 5 | 24 | |
|
|
| 6 | 24 | |
|
|
| 7 | 24 | |
|
|
| 8 | 24 | |
|
|
| 9 | 24 | |
|
|
| 10 | 24 | |
|
|
| 11 | 24 | |
|
|
| 12 | 24 | |
|
|
| 13 | 24 | |
|
|
|
|
|
### Training Hyperparameters |
|
|
- batch_size: (16, 16) |
|
|
- num_epochs: (1, 1) |
|
|
- max_steps: -1 |
|
|
- sampling_strategy: oversampling |
|
|
- num_iterations: 99 |
|
|
- body_learning_rate: (0.0002733656643765287, 0.0002733656643765287) |
|
|
- head_learning_rate: 2.7029049129688732e-05 |
|
|
- loss: CosineSimilarityLoss |
|
|
- distance_metric: cosine_distance |
|
|
- margin: 0.25 |
|
|
- end_to_end: False |
|
|
- use_amp: False |
|
|
- warmup_proportion: 0.1 |
|
|
- max_length: 512 |
|
|
- seed: 42 |
|
|
- eval_max_steps: -1 |
|
|
- load_best_model_at_end: True |
|
|
|
|
|
### Training Results |
|
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|
|:-------:|:--------:|:-------------:|:---------------:| |
|
|
| 0.0002 | 1 | 0.2546 | - | |
|
|
| 0.0120 | 50 | 0.1667 | - | |
|
|
| 0.0241 | 100 | 0.1165 | - | |
|
|
| 0.0361 | 150 | 0.0799 | - | |
|
|
| 0.0481 | 200 | 0.0212 | - | |
|
|
| 0.0601 | 250 | 0.0188 | - | |
|
|
| 0.0722 | 300 | 0.0531 | - | |
|
|
| 0.0842 | 350 | 0.0273 | - | |
|
|
| 0.0962 | 400 | 0.0111 | - | |
|
|
| 0.1082 | 450 | 0.0203 | - | |
|
|
| 0.1203 | 500 | 0.0397 | - | |
|
|
| 0.1323 | 550 | 0.0164 | - | |
|
|
| 0.1443 | 600 | 0.0045 | - | |
|
|
| 0.1563 | 650 | 0.0032 | - | |
|
|
| 0.1684 | 700 | 0.001 | - | |
|
|
| 0.1804 | 750 | 0.0011 | - | |
|
|
| 0.1924 | 800 | 0.0004 | - | |
|
|
| 0.2044 | 850 | 0.0009 | - | |
|
|
| 0.2165 | 900 | 0.0006 | - | |
|
|
| 0.2285 | 950 | 0.0008 | - | |
|
|
| 0.2405 | 1000 | 0.0004 | - | |
|
|
| 0.2525 | 1050 | 0.0008 | - | |
|
|
| 0.2646 | 1100 | 0.0005 | - | |
|
|
| 0.2766 | 1150 | 0.0006 | - | |
|
|
| 0.2886 | 1200 | 0.0007 | - | |
|
|
| 0.3006 | 1250 | 0.0043 | - | |
|
|
| 0.3127 | 1300 | 0.0004 | - | |
|
|
| 0.3247 | 1350 | 0.0005 | - | |
|
|
| 0.3367 | 1400 | 0.0005 | - | |
|
|
| 0.3487 | 1450 | 0.0004 | - | |
|
|
| 0.3608 | 1500 | 0.0004 | - | |
|
|
| 0.3728 | 1550 | 0.0005 | - | |
|
|
| 0.3848 | 1600 | 0.0007 | - | |
|
|
| 0.3968 | 1650 | 0.0006 | - | |
|
|
| 0.4089 | 1700 | 0.0002 | - | |
|
|
| 0.4209 | 1750 | 0.0006 | - | |
|
|
| 0.4329 | 1800 | 0.0008 | - | |
|
|
| 0.4449 | 1850 | 0.0003 | - | |
|
|
| 0.4570 | 1900 | 0.0005 | - | |
|
|
| 0.4690 | 1950 | 0.0003 | - | |
|
|
| 0.4810 | 2000 | 0.0003 | - | |
|
|
| 0.4930 | 2050 | 0.0003 | - | |
|
|
| 0.5051 | 2100 | 0.0006 | - | |
|
|
| 0.5171 | 2150 | 0.0003 | - | |
|
|
| 0.5291 | 2200 | 0.0002 | - | |
|
|
| 0.5411 | 2250 | 0.0002 | - | |
|
|
| 0.5532 | 2300 | 0.0002 | - | |
|
|
| 0.5652 | 2350 | 0.0004 | - | |
|
|
| 0.5772 | 2400 | 0.0003 | - | |
|
|
| 0.5892 | 2450 | 0.0003 | - | |
|
|
| 0.6013 | 2500 | 0.0002 | - | |
|
|
| 0.6133 | 2550 | 0.0002 | - | |
|
|
| 0.6253 | 2600 | 0.0013 | - | |
|
|
| 0.6373 | 2650 | 0.0002 | - | |
|
|
| 0.6494 | 2700 | 0.0007 | - | |
|
|
| 0.6614 | 2750 | 0.0004 | - | |
|
|
| 0.6734 | 2800 | 0.0007 | - | |
|
|
| 0.6854 | 2850 | 0.0018 | - | |
|
|
| 0.6975 | 2900 | 0.0002 | - | |
|
|
| 0.7095 | 2950 | 0.0003 | - | |
|
|
| 0.7215 | 3000 | 0.0006 | - | |
|
|
| 0.7335 | 3050 | 0.0003 | - | |
|
|
| 0.7456 | 3100 | 0.0002 | - | |
|
|
| 0.7576 | 3150 | 0.0002 | - | |
|
|
| 0.7696 | 3200 | 0.0002 | - | |
|
|
| 0.7816 | 3250 | 0.0002 | - | |
|
|
| 0.7937 | 3300 | 0.0002 | - | |
|
|
| 0.8057 | 3350 | 0.0001 | - | |
|
|
| 0.8177 | 3400 | 0.0003 | - | |
|
|
| 0.8297 | 3450 | 0.0002 | - | |
|
|
| 0.8418 | 3500 | 0.0002 | - | |
|
|
| 0.8538 | 3550 | 0.0002 | - | |
|
|
| 0.8658 | 3600 | 0.0002 | - | |
|
|
| 0.8778 | 3650 | 0.0002 | - | |
|
|
| 0.8899 | 3700 | 0.0002 | - | |
|
|
| 0.9019 | 3750 | 0.0005 | - | |
|
|
| 0.9139 | 3800 | 0.0002 | - | |
|
|
| 0.9259 | 3850 | 0.0001 | - | |
|
|
| 0.9380 | 3900 | 0.0004 | - | |
|
|
| 0.9500 | 3950 | 0.0001 | - | |
|
|
| 0.9620 | 4000 | 0.0005 | - | |
|
|
| 0.9740 | 4050 | 0.0002 | - | |
|
|
| 0.9861 | 4100 | 0.0002 | - | |
|
|
| 0.9981 | 4150 | 0.0001 | - | |
|
|
| **1.0** | **4158** | **-** | **0.0302** | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
### Framework Versions |
|
|
- Python: 3.10.12 |
|
|
- SetFit: 1.1.0.dev0 |
|
|
- Sentence Transformers: 3.0.1 |
|
|
- Transformers: 4.42.4 |
|
|
- PyTorch: 2.3.1+cu121 |
|
|
- Datasets: 2.20.0 |
|
|
- 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} |
|
|
} |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
## Glossary |
|
|
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## Model Card Authors |
|
|
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## Model Card Contact |
|
|
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
|
--> |
