metadata
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: >-
mine employment is starker at mid-:The future of coal mine employment is
starker at mid-century.
- text: >-
are considered relatively noisy - a major:But the Type 094s, which carry
China's most advanced submarine-launched JL-3 missile, are considered
relatively noisy - a major handicap for military submarines.
- text: >-
March this year raised an objection, saying that:Initially, the
registrar's office of the IHC in March this year raised an objection,
saying that the high court was not an appropriate forum and asked the
petitioner to approach the relevant authorities.
- text: >-
mine closures is West Bengal.:Runa Sarkar, a professor at the Indian
Institute of Management Calcutta, said the coal mining region most
affected by mine closures is West Bengal.
- text: >-
FIA DG to proceed in accordance with the law.:When the petition was heard
by the chief justice, he asked the FIA DG to proceed in accordance with
the law.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Polarity Model with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7065217391304348
name: Accuracy
SetFit Polarity Model with BAAI/bge-small-en-v1.5
This is a SetFit model 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 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: BAAI/bge-small-en-v1.5
- Classification head: a LogisticRegression instance
- spaCy Model: en_core_web_lg
- SetFitABSA Aspect Model: asadnaqvi/setfitabsa-aspect
- SetFitABSA Polarity Model: asadnaqvi/setfitabsa-polarity
- Maximum Sequence Length: 512 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 |
|---|---|
| Informative |
|
| Negative |
|
| Positive |
|
| Ambivalent |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.7065 |
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(
"asadnaqvi/setfitabsa-aspect",
"asadnaqvi/setfitabsa-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 | 11 | 27.7071 | 45 |
| Label | Training Sample Count |
|---|---|
| Ambivalent | 1 |
| Informative | 73 |
| Negative | 20 |
| Positive | 5 |
Training Hyperparameters
- batch_size: (128, 128)
- 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 |
|---|---|---|---|
| 0.0217 | 1 | 0.2599 | - |
| 1.0870 | 50 | 0.0608 | 0.3526 |
| 2.1739 | 100 | 0.0253 | 0.4091 |
| 3.2609 | 150 | 0.0159 | 0.4497 |
| 4.3478 | 200 | 0.0035 | 0.4437 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
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}
}