metadata

language: en
license: apache-2.0
library_name: setfit
tags:
  - setfit
  - absa
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
datasets:
  - tomaarsen/setfit-absa-semeval-restaurants
metrics:
  - accuracy
widget:
  - text: bottles of wine:bottles of wine are cheap and good.
  - text: world:I also ordered the Change Mojito, which was out of this world.
  - text: >-
      bar:We were still sitting at the bar while we drank the sangria, but
      facing away from the bar when we turned back around, the $2 was gone the
      people next to us said the bartender took it.
  - text: >-
      word:word of advice, save room for pasta dishes and never leave until
      you've had the tiramisu.
  - text: >-
      bartender:We were still sitting at the bar while we drank the sangria, but
      facing away from the bar when we turned back around, the $2 was gone the
      people next to us said the bartender took it.
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
  emissions: 18.322516829847984
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.303
  hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: BAAI/bge-small-en-v1.5
model-index:
  - name: >-
      SetFit Aspect Model with BAAI/bge-small-en-v1.5 on SemEval 2014 Task 4
      (Restaurants)
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: SemEval 2014 Task 4 (Restaurants)
          type: tomaarsen/setfit-absa-semeval-restaurants
          split: test
        metrics:
          - type: accuracy
            value: 0.8623188405797102
            name: Accuracy

SetFit Aspect Model with BAAI/bge-small-en-v1.5 on SemEval 2014 Task 4 (Restaurants)

This is a SetFit model trained on the SemEval 2014 Task 4 (Restaurants) dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression 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: BAAI/bge-small-en-v1.5
Classification head: a LogisticRegression instance
spaCy Model: en_core_web_lg
SetFitABSA Aspect Model: tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect
SetFitABSA Polarity Model: tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes
Training Dataset: SemEval 2014 Task 4 (Restaurants)
Language: en
License: apache-2.0

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	'staff:But the staff was so horrible to us.' "food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora." "food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."
no aspect	"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora." "deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora." "Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."

Label

Examples

aspect

'staff:But the staff was so horrible to us.'
"food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
"food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."

no aspect

"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
"deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."
"Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."

Evaluation

Metrics

Label	Accuracy
all	0.8623

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(
    "tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-aspect",
    "tomaarsen/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	4	19.3576	45

Label	Training Sample Count
no aspect	170
aspect	255

Training Hyperparameters

batch_size: (256, 256)
num_epochs: (5, 5)
max_steps: 5000
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
0.0027	1	0.2498	-
0.1355	50	0.2442	-
0.2710	100	0.2462	0.2496
0.4065	150	0.2282	-
0.5420	200	0.0752	0.1686
0.6775	250	0.0124	-
0.8130	300	0.0128	0.1884
0.9485	350	0.0062	-
1.0840	400	0.0012	0.183
1.2195	450	0.0009	-
1.3550	500	0.0008	0.2072
1.4905	550	0.0031	-
1.6260	600	0.0006	0.1716
1.7615	650	0.0005	-
1.8970	700	0.0005	0.1666
2.0325	750	0.0005	-
2.1680	800	0.0004	0.2086
2.3035	850	0.0005	-
2.4390	900	0.0004	0.183
2.5745	950	0.0004	-
2.7100	1000	0.0036	0.1725
2.8455	1050	0.0004	-
2.9810	1100	0.0003	0.1816
3.1165	1150	0.0004	-
3.2520	1200	0.0003	0.1802

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.018 kg of CO2
Hours Used: 0.303 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.9.16
SetFit: 1.0.0.dev0
Sentence Transformers: 2.2.2
spaCy: 3.7.2
Transformers: 4.29.0
PyTorch: 1.13.1+cu117
Datasets: 2.15.0
Tokenizers: 0.13.3

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}
}