|
|
--- |
|
|
library_name: setfit |
|
|
tags: |
|
|
- setfit |
|
|
- sentence-transformers |
|
|
- text-classification |
|
|
- generated_from_setfit_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
widget: |
|
|
- text: 'The Alavas worked themselves to the bone in the last period , and English |
|
|
and San Emeterio ( 65-75 ) had already made it clear that they were not going |
|
|
to let anyone take away what they had earned during the first thirty minutes . ' |
|
|
- text: 'To break the uncomfortable silence , Haney began to talk . ' |
|
|
- text: 'For the treatment of non-small cell lung cancer , the effects of Alimta were |
|
|
compared with those of docetaxel ( another anticancer medicine ) in one study |
|
|
involving 571 patients with locally advanced or metastatic disease who had received |
|
|
chemotherapy in the past . ' |
|
|
- text: 'As we all know , a few minutes before the end of the game ( that their team |
|
|
had already won ) , both players deliberately wasted time which made the referee |
|
|
show the second yellow card to both of them . ' |
|
|
- text: 'In contrast , patients whose cancer was affecting squamous cells had shorter |
|
|
survival times if they received Alimta . ' |
|
|
pipeline_tag: text-classification |
|
|
inference: true |
|
|
base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
|
|
--- |
|
|
|
|
|
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
|
|
|
|
|
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
|
|
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 | |
|
|
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| 6 | <ul><li>'3 -RRB- Republican congressional representatives , because of their belief in a minimalist state , are less willing to engage in local benefit-seeking than are Democratic members of Congress . '</li><li>'That is the way the system works . '</li><li>'Duck swarms . '</li></ul> | |
|
|
| 2 | <ul><li>'It explains how the Committee for Medicinal Products for Veterinary Use ( CVMP ) assessed the studies performed , to reach their recommendations on how to use the medicine . '</li><li>'Tricks such as those of Alonso and Ramos before the Ajax demonstrate wittiness but not the will to get remove of a sanction . '</li><li>'The next day , Sunday , the hangover reminded Haney where he had been the night before . '</li></ul> | |
|
|
| 3 | <ul><li>'If it is , it will be treated as an operator , if it is not , it will be treated as a user function . '</li><li>'Back in the chase car , we drove around some more , got stuck in a ditch , enlisted the aid of a local farmer to get out the trailer hitch and pull us out of the ditch . '</li><li>"It was the most exercise we 'd had all morning and it was followed by our driving immediately to the nearest watering hole . "</li></ul> | |
|
|
| 5 | <ul><li>'The discovery of a strange bacteria that can use arsenic as one of its nutrients widens the scope for finding new forms of life on Earth and possibly beyond . '</li><li>'I felt the temblor begin and glanced at the table next to mine , smiled that guilty smile and we both mouthed the words , `` Earth-quake ! `` together . '</li><li>'Already two major pharmaceutical companies , the Squibb unit of Bristol-Myers Squibb Co. and Hoffmann-La Roche Inc. , are collaborating with gene hunters to turn the anticipated cascade of discoveries into predictive tests and , maybe , new therapies . '</li></ul> | |
|
|
| 0 | <ul><li>'Prior to 1932 , the pattern was nearly the opposite . '</li><li>'A minor contrast to Costa Rica , comparing the 22 players called by both countries for the friendly game today , at 3:05 pm at the National Stadium in San Jose . '</li><li>'Never in my life have I been so frightened . '</li></ul> | |
|
|
| 4 | <ul><li>'`` To ring for even one service at this tower , we have to scrape , `` says Mr. Hammond , a retired water-authority worker . `` '</li><li>'It is a passion that usually stays in the tower , however . '</li><li>'One writer , signing his letter as `` Red-blooded , balanced male , `` remarked on the `` frequency of women fainting in peals , `` and suggested that they `` settle back into their traditional role of making tea at meetings . `` '</li></ul> | |
|
|
| 1 | <ul><li>'Bribe by bribe , Mr. Sternberg and his co-author , Matthew C. Harrison Jr. , lead us along the path Wedtech traveled , from its inception as a small manufacturing company to the status of full-fledged defense contractor , entrusted with the task of producing vital equipment for the Army and Navy . '</li><li>"kalgebra 's console is useful as a calculator . "</li><li>'Then a wild thought ran circles through his clouded brain . '</li></ul> | |
|
|
|
|
|
## 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("HelgeKn/SemEval-multi-class-10") |
|
|
# Run inference |
|
|
preds = model("To break the uncomfortable silence , Haney began to talk . ") |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### 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 | 4 | 28.1286 | 74 | |
|
|
|
|
|
| Label | Training Sample Count | |
|
|
|:------|:----------------------| |
|
|
| 0 | 10 | |
|
|
| 1 | 10 | |
|
|
| 2 | 10 | |
|
|
| 3 | 10 | |
|
|
| 4 | 10 | |
|
|
| 5 | 10 | |
|
|
| 6 | 10 | |
|
|
|
|
|
### Training Hyperparameters |
|
|
- batch_size: (16, 16) |
|
|
- num_epochs: (2, 2) |
|
|
- max_steps: -1 |
|
|
- sampling_strategy: oversampling |
|
|
- num_iterations: 20 |
|
|
- body_learning_rate: (2e-05, 2e-05) |
|
|
- head_learning_rate: 2e-05 |
|
|
- 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: False |
|
|
|
|
|
### Training Results |
|
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|
|:------:|:----:|:-------------:|:---------------:| |
|
|
| 0.0057 | 1 | 0.2488 | - | |
|
|
| 0.2857 | 50 | 0.2041 | - | |
|
|
| 0.5714 | 100 | 0.1094 | - | |
|
|
| 0.8571 | 150 | 0.0478 | - | |
|
|
| 1.1429 | 200 | 0.0378 | - | |
|
|
| 1.4286 | 250 | 0.0089 | - | |
|
|
| 1.7143 | 300 | 0.0036 | - | |
|
|
| 2.0 | 350 | 0.0029 | - | |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.9.13 |
|
|
- SetFit: 1.0.1 |
|
|
- Sentence Transformers: 2.2.2 |
|
|
- Transformers: 4.36.0 |
|
|
- PyTorch: 2.1.1+cpu |
|
|
- Datasets: 2.15.0 |
|
|
- Tokenizers: 0.15.0 |
|
|
|
|
|
## 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.* |
|
|
--> |
