File size: 10,381 Bytes
e20d2d6 2ee875d e20d2d6 2ee875d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 'investors:Creating huge opportunities for investors who can see past this
rate hike cycle. Which should be over soon. #tesla $TSLA'
- text: 'Powerwall:@TeslaSolar roof stood up to #HurricaneIan with 155mph winds and
storm surge! This Powerwall was underwater for hours and is still working perfectly.'
- text: 'analysts:Ron Barron: We see so much potential, we don’t want to sell; Of
all companies I cover & analysts come pitch to me, the company I feel the
most confidence in is $TSLA; People think we''re going into a slowdown but demand
for their cars has never been better.'
- text: house:Thank you @Tesla for delivering a car to the wrong address (my house),
blocking my driveway for hours and not allowing me to pickup my kids from school
on such a hot day. On top of that, all your driver had to say was "call Tesla
and tell them, I'm just a driver". https://t.co/QqMXoAT7SJ
- text: 'pitch:Ron Barron: We see so much potential, we don’t want to sell; Of all
companies I cover & analysts come pitch to me, the company I feel the most
confidence in is $TSLA; People think we''re going into a slowdown but demand for
their cars has never been better.'
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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. In particular, this model is in charge of filtering aspect span candidates.
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.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## 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
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [NazmusAshrafi/setfit-absa-sm-stock-tweet-aspect](https://huggingface.co/NazmusAshrafi/setfit-absa-sm-stock-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/setfit-absa-sm-stock-tweet-polarity](https://huggingface.co/NazmusAshrafi/setfit-absa-sm-stock-tweet-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 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 |
|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | <ul><li>'staff:But the staff was so horrible to us.'</li><li>'@WholeMarsBlog:For years @WholeMarsBlog viciously silenced @Tesla critics. Failing to silence me, he desperately lashes out with childish insults about me, my company, my products - and even my His fear and impotence spurs me on to ensure that everyone understands Full Self-Driving is Apple.'</li><li>'Apple:For years @WholeMarsBlog viciously silenced @Tesla critics. Failing to silence me, he desperately lashes out with childish insults about me, my company, my products - and even my His fear and impotence spurs me on to ensure that everyone understands Full Self-Driving is Apple.'</li></ul> |
| no aspect | <ul><li>'Tesla delivery estimates:Tesla delivery estimates are at around 364k from the analysts.'</li><li>'analysts:Tesla delivery estimates are at around 364k from the analysts.'</li><li>'@Tesla critics:For years @WholeMarsBlog viciously silenced @Tesla critics. Failing to silence me, he desperately lashes out with childish insults about me, my company, my products - and even my His fear and impotence spurs me on to ensure that everyone understands Full Self-Driving is Apple.'</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 AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"NazmusAshrafi/setfit-absa-sm-stock-tweet-aspect",
"NazmusAshrafi/setfit-absa-sm-stock-tweet-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
<!--
### 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 | 34.3860 | 54 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 93 |
| aspect | 21 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0017 | 1 | 0.2658 | - |
| 0.0868 | 50 | 0.2171 | - |
| 0.1736 | 100 | 0.0649 | - |
| 0.2604 | 150 | 0.0259 | - |
| 0.3472 | 200 | 0.0802 | - |
| 0.4340 | 250 | 0.0425 | - |
| 0.5208 | 300 | 0.0258 | - |
| 0.6076 | 350 | 0.0435 | - |
| 0.6944 | 400 | 0.0793 | - |
| 0.7812 | 450 | 0.0072 | - |
| 0.8681 | 500 | 0.0003 | - |
| 0.9549 | 550 | 0.0116 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.2
- Sentence Transformers: 2.2.2
- spaCy: 3.6.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- 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.*
--> |