metadata
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
library_name: setfit
metrics:
- f1
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Locale:Locale molto bene arredato, con stile e atmosfera tipica
valtellinese. Cucina ottima, dal bastone di carne al pesce, dai
pizzoccheri agli gnocchetti, dal vino ai dolci, tutto perfetto e
soprattutto di grande qualità... Filippo poi è un’autentica forza della
natura, molto simpatico, cordiale e amichevole,...Altro
- text: >-
cucina:Locale accogliente e familiare...bravissima la ragazza in cucina,
come le ragazze al banco e in sala! CONSIGLIATO
- text: servizio:Il servizio era impeccabile e il tortello di zucca era sublime.
- text: >-
cucina:Il ristorante propone piatti vegetariani che NON sono vegetariani.
Dopo aver specificato al servizio la nostra etica alimentare, ci è stata
consigliata una portata che durante la consumazione abbiamo constatato con
amarezza che avesse parti di maiale come ingredienti (confermato dalla
cucina). Poco valgono le...scuse del servizio, trovo assurdo e
inconcepibile che situazioni del genere possano accadere nel 2024.
Evidentemente questo è indice della poca professionalità di questo
ristorante.Altro
- text: >-
servizio:La polenta con formaggio era saporita, ma il servizio è stato
lento.
inference: false
model-index:
- name: >-
SetFit Aspect Model with
sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: f1
value: 0.8096514745308312
name: F1
SetFit Aspect Model with sentence-transformers/paraphrase-multilingual-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-multilingual-mpnet-base-v2 as the Sentence Transformer embedding model. A SetFitHead 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:
- 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 this SetFit model to filter these possible aspect span candidates.
- Use a SetFit model to classify the filtered aspect span candidates.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
- Classification head: a SetFitHead instance
- spaCy Model: it_core_news_lg
- SetFitABSA Aspect Model: MattiaTintori/Final_aspect_Colab_It
- SetFitABSA Polarity Model: setfit-absa-polarity
- Maximum Sequence Length: 128 tokens
- Number of Classes: 2 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 |
|---|---|
| aspect |
|
| no aspect |
|
Evaluation
Metrics
| Label | F1 |
|---|---|
| all | 0.8097 |
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(
"MattiaTintori/Final_aspect_Colab_It",
"setfit-absa-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 | 9 | 40.3192 | 137 |
| Label | Training Sample Count |
|---|---|
| no aspect | 1379 |
| aspect | 1378 |
Training Hyperparameters
- batch_size: (128, 32)
- num_epochs: (5, 32)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (5e-05, 5e-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
- l2_weight: 0.02
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0023 | 1 | 0.2484 | - |
| 0.0464 | 20 | 0.2718 | 0.259 |
| 0.0928 | 40 | 0.2581 | 0.2544 |
| 0.1392 | 60 | 0.2266 | 0.2475 |
| 0.1856 | 80 | 0.233 | 0.2298 |
| 0.2320 | 100 | 0.2104 | 0.2145 |
| 0.2784 | 120 | 0.1487 | 0.2106 |
| 0.3248 | 140 | 0.1615 | 0.2314 |
| 0.3712 | 160 | 0.1328 | 0.2164 |
| 0.4176 | 180 | 0.0905 | 0.2164 |
| 0.4640 | 200 | 0.0934 | 0.2517 |
| 0.5104 | 220 | 0.0942 | 0.2185 |
| 0.5568 | 240 | 0.0774 | 0.2469 |
| 0.6032 | 260 | 0.1013 | 0.2248 |
| 0.6497 | 280 | 0.0781 | 0.2221 |
| 0.6961 | 300 | 0.0386 | 0.2362 |
| 0.7425 | 320 | 0.084 | 0.2386 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.1.0
- spaCy: 3.7.6
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 3.0.0
- 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}
}