metadata
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
; got my car in about a:Well.. I added a new 🐎 to the stable! Special
thanks to Matt at the @Tesla Clarkston location who made my Model Y order
& delivery incredibly smooth.🙏 I'm super lucky & got my car in
about a week of deciding to go for it 😳 Video coming soon about that
process & more! https://t.co/PrP91xMnKk
- text: >-
. But the price could be cheaper:C’mon @elonmusk! Australians are busting
to buy EVs & the best one is @Tesla imho. But the price could be
cheaper, if you built a #gigafactory in Australia. 70% of the lithium in
the cars is #aussie so why not set up a #gigafactorydownunder? All the
talent and minerals are here!
- text: >-
generate more net profit from legacy auto:As with previous quarters, $TSLA
will generate more net profit from legacy auto regulatory credits sales
this quarter than legacy auto will make in gross profit by selling EVs.
This just keeps adding insult to injury.
- text: >-
on keeping this car for 10 years:@_brivnii @Tesla I plan on keeping this
car for 10 years total (so 6 more years at least). I don't feel the need
to upgrade to a newer model even if price is no issue. This one has been
reliable, and I got a good battery (no signs of degradation so far)
- text: >-
The driver’s car was a @Tesla:I took an @Uber home from the airport and my
bill had a fuel surcharge on it because of the current price of gasoline.
The driver’s car was a @Tesla… 🤷
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: 0.44
name: Accuracy
SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- spaCy Model: en_core_web_lg
- SetFitABSA Aspect Model: NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect
- SetFitABSA Polarity Model: NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-polarity
- Maximum Sequence Length: 512 tokens
- Number of Classes: 3 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 |
|---|---|
| neutral |
|
| negative |
|
| positive |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.44 |
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(
"NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-aspect",
"NazmusAshrafi/setfit-MiniLM-mpnet-absa-tesla-tweet-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 | 26 | 46.2121 | 61 |
| Label | Training Sample Count |
|---|---|
| negative | 11 |
| neutral | 12 |
| positive | 10 |
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 | 0.186 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- spaCy: 3.6.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1
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}
}