File size: 10,072 Bytes
645db96 11238cf 645db96 11238cf |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: The food at Cafe Asean:The food at Cafe Asean is to die for, and the prices
are unmatchable.
- text: its a cool place to come with:its a cool place to come with a bunch of people
or with a date for maybe a mild dinner or some drinks.
- text: times, the food is always good:Although the service can be a bit brusque at
times, the food is always good, hearty and hot.
- text: we found the food to be so:Came recommended to us, but we found the food to
be so-so, the service good, but we were told we could not order desert since the
table we were at had a reservation waiting.
- text: warned that this place can get pretty:Be warned that this place can get pretty
crowded, though the $3 bloody mary's at the bar and the killer DJ make the wait
more than bearable.
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.45161290322580644
name: Accuracy
---
# SetFit Polarity 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 classifying aspect polarities.
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 a SetFit model to filter these possible aspect span candidates.
3. **Use this 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/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect](https://huggingface.co/NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity](https://huggingface.co/NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity)
- **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 |
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>"best sit down food I've had:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."</li><li>'generous and the staff brings out multiple:Portions are fairly generous and the staff brings out multiple little bites and treats throughout dinner.'</li><li>'casual Middle Eastern menu looks familar,:The Food The casual Middle Eastern menu looks familar, but the food--made to order in the open kitchen--is a notch above its peers.'</li></ul> |
| neutral | <ul><li>"be just the place for you.:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."</li><li>') other food is served in:) other food is served in too-small portions, but at least it leaves room for dessert.'</li><li>"room while the food on other peoples:Upon entering, I was impressed by the room while the food on other peoples' tables seemed enticing."</li></ul> |
| negative | <ul><li>'Though the service might be a:Though the service might be a little slow, the waitresses are very friendly.'</li><li>'was expecting poor service and ambience but:After reading other reviews I was expecting poor service and ambience but was pleasantly surprised by our more than helpful waiter.'</li><li>'we found the food to be so:Came recommended to us, but we found the food to be so-so, the service good, but we were told we could not order desert since the table we were at had a reservation waiting.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.4516 |
## 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/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect",
"NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-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 | 17 | 28.6364 | 53 |
| Label | Training Sample Count |
|:---------|:----------------------|
| negative | 10 |
| neutral | 12 |
| positive | 11 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0217 | 1 | 0.2212 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- spaCy: 3.7.4
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- 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.*
--> |