metadata
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
ordered, great atmosphere, excellent service:FINALLY tried Mizza and
wasn't disappointed. Loved (almost) everything we ordered, great
atmosphere, excellent service, and the perfect setting for a lovely bday
Sunday. The burrata & heirloom tomatoes app was scrumptious, the salmon
pasta, very flavorful and the salmon perfectly cooked, I liked the
toppings of the veggie pizza but wasn't a super fan of the crust (doesn't
mean I won't come back and try another pizza on their menu ) and the
cannoli was good although that dessert in general isn't my fave (it was my
bf's bday so had to get what he wanted ). The flourless chocolate cake and
limoncello cake are what I'll try next time. Had a great time and will be
back. Gave it 4 stars just cuz I wasn't that excited about the pizza and
that's something they're supposed to so well. Would recommend the
restaurant though!
- text: >-
is because the food was decent,:Three reasons why it gets three stars:
1. The crab cakes were good and is a definitely must try!
2. The shrimp scampi was actually amazing in the sauce that it comes with,
so that's another must try!
3. The real reason why it is getting three stars is because service is
everything in ANY restaurant you go to. Service started off great,
waitress was attentive, but once we paid the bill and left a 20% tip, my
guests and I, which was only three of us, stayed at the table to finish
our drinks and we're looking at funny videos from a trip we went to. Point
is the waitress rudely told my friend to lower the volume on his phone,
yet other guests were just as loud and we were sitting OUTSIDE...where it
is already a loud environment!
I really want to give it 4 stars, but if I give 4 stars it changes it to,
"Yay! I'm a fan", but I am not. The only reason why it's not getting 1
star, is because the food was decent, the view is nice and also the
manager was extremely empathetic to the situation and it wasn't her fault
at all that her waitress was obviously having an off day. I have never met
a manager that attentive and she was incredible at handling and diffusing
the situation. I cannot thank her enough for salvaging the rest of our
evening for how poor the waitress treated paying customers.
- text: >-
and the perfect setting for a lovely:FINALLY tried Mizza and wasn't
disappointed. Loved (almost) everything we ordered, great atmosphere,
excellent service, and the perfect setting for a lovely bday Sunday. The
burrata & heirloom tomatoes app was scrumptious, the salmon pasta, very
flavorful and the salmon perfectly cooked, I liked the toppings of the
veggie pizza but wasn't a super fan of the crust (doesn't mean I won't
come back and try another pizza on their menu ) and the cannoli was good
although that dessert in general isn't my fave (it was my bf's bday so had
to get what he wanted ). The flourless chocolate cake and limoncello cake
are what I'll try next time. Had a great time and will be back. Gave it 4
stars just cuz I wasn't that excited about the pizza and that's something
they're supposed to so well. Would recommend the restaurant though!
- text: >-
) and the service is friendly and:I'm not sure what what I would do if I'd
never discovered Nikka, since it's the definitely the most authentic ramen
one can get in the area. Prices are standard for ramen (especially in SB)
and the service is friendly and efficient. Not only is Nikka's ramen
amazing, their variety of appetizers is also great. I've yet to try one
that I don't like. Definitely come here if you're looking to satisfy your
ramen craving!
- text: >-
Overall an excellent experience and the friendly:I got a to-go order for
empanadas on the lunch menu and it was fantastic. The dish was incredibly
flavorful and the Kombucha the owner recommended was amazing. Overall an
excellent experience and the friendly owner, waiters, and waitresses are
just the cherry on top. I would highly recommend any vegetarians to try
out this spot!
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.65
name: Accuracy
SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression 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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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:
- Use a spaCy model to select possible aspect span candidates.
- Use a SetFit model to filter these possible aspect span candidates.
- Use this SetFit model to classify the filtered aspect span candidates.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
- Classification head: a LogisticRegression instance
- spaCy Model: en_core_web_sm
- SetFitABSA Aspect Model: ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect
- SetFitABSA Polarity Model: ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity
- Maximum Sequence Length: 384 tokens
- Number of Classes: 4 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| negative |
|
| positive |
|
| mixed |
|
| neutral |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.65 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-aspect",
"ginkgogo/setfit-absa-bge-small-en-v1.5-restaurants-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 25 | 123.9048 | 272 |
| Label | Training Sample Count |
|---|---|
| mixed | 1 |
| negative | 1 |
| neutral | 1 |
| positive | 18 |
Training Hyperparameters
- batch_size: (50, 50)
- num_epochs: (5, 5)
- 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: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.1429 | 1 | 0.2034 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.0
- spaCy: 3.7.4
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
Citation
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}
}