|
--- |
|
library_name: setfit |
|
tags: |
|
- setfit |
|
- sentence-transformers |
|
- text-classification |
|
- generated_from_setfit_trainer |
|
metrics: |
|
- accuracy |
|
widget: |
|
- text: 'Palestinians throughout the West Bank know that the arrival of a bulldozer |
|
means the same thing time and time again: "You have 24 hours to flee, or we will |
|
shoot you." There are countless towns/villages/communities that have faced demolitions |
|
by the IOF throughout the decades of Israel''s existence, I couldn''t even begin |
|
to name all of them here.' |
|
- text: 'For now, let?s remember a few pertinent points about a ceasefire in the Israel-Hamas |
|
war:' |
|
- text: Would UNC have to then divest from portfolio boosting stocks like Amazon or |
|
even Coca-Cola since Israelis buy the soft drink? |
|
- text: The Armenian quarter is not safe from settler encroachment either, as demolitions |
|
in the West Bank continue, real estate companies have sent in settlers and bulldozers |
|
to steal land belonging to Armenian Church property and Several Armenian families. |
|
- text: In response, Intel has said that profit margins could return to historically |
|
high levels within five years. |
|
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 [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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
|
- **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:** 3 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 | |
|
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| critical | <ul><li>' * Walk out from work and/or school * Picket Israeli embassies and consulates * Picket against companies that profit from Israel?s occupation of Palestine (Lockheed Martin, Boeing, Raytheon, Northrop Grumman, General Dynamics, Elbit Systems) * Host speak outs * Wear kuffiyehs * Wear black armbands'</li><li>"(Nov. 2) Thread of demonstrations in solidarity with Palestinians, via @LexiAlex: U.S., U.K., U.S., U.K., South Africa, Australia, Canada, U.S. Cool and all but I don't think Raytheon cares there's blood on their hands."</li><li>'99% of computers have intel processors, 100% of which are made with Israeli tech, 99% of which are manufactured in israel lmao and that?s just intel!'</li></ul> | |
|
| neutral | <ul><li>" Intel secures $3.25B Israeli gov't grant to build $25B chip fab in Israel amid ongoing tensions : Read more"</li><li>'? Austin noted some defense contractors have required workers to take on additional shifts to keep up with production rates.'</li><li>'>?Germany?s leading role in NATO matters at this critical moment for European security,?'</li></ul> | |
|
| negative | <ul><li>'? Sister of Israeli hostage Elad Katzir says her brother was murdered in captivity, and his body was recovered in Gaza during a military rescue operation.'</li><li>'"I think this is something that goes beyond what you would normally consider politics, in the sense that it\'s been hard for anyone to keep up with the rest of the world, and ignore the fact that every single university in Gaza has been flattened, the fact that hospitals have been destroyed, the fact that 14,500 children have died." The event ran two and a half-hours, and not without dissent from a boisterous group of counter-protestors along the west side of the plaza, less organized, shouting "USA," "Take a shower," "Go back to Russia" and "Stop supporting terrorism," some literally wrapped in U.'</li><li>'"There\'s been panic everywhere - even here in Khan Younis where the bombing was less - as people try to reach family members in other areas to check they are safe, but the phones have been cut off." There was anger as well as fear from the families of the Gaza hostages.'</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("setfit_model_id") |
|
# Run inference |
|
preds = model("For now, let?s remember a few pertinent points about a ceasefire in the Israel-Hamas war:") |
|
``` |
|
|
|
<!-- |
|
### 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 | 7 | 29.3647 | 111 | |
|
|
|
| Label | Training Sample Count | |
|
|:---------|:----------------------| |
|
| critical | 24 | |
|
| negative | 26 | |
|
| neutral | 35 | |
|
|
|
### Training Hyperparameters |
|
- batch_size: (16, 2) |
|
- num_epochs: (1, 16) |
|
- 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: False |
|
|
|
### Training Results |
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|:------:|:----:|:-------------:|:---------------:| |
|
| 0.0034 | 1 | 0.3409 | - | |
|
| 0.1684 | 50 | 0.1854 | - | |
|
| 0.3367 | 100 | 0.0944 | - | |
|
| 0.5051 | 150 | 0.035 | - | |
|
| 0.6734 | 200 | 0.0021 | - | |
|
| 0.8418 | 250 | 0.0011 | - | |
|
|
|
### Framework Versions |
|
- Python: 3.10.6 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 3.0.0 |
|
- Transformers: 4.35.2 |
|
- PyTorch: 2.2.0 |
|
- Datasets: 2.14.4 |
|
- 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## 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.* |
|
--> |
