metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: |2+
I'll pay $1,000 if anyone can find a published study that ChatGPT confirms merely attempts to refute the OPV AIDS theory without desperately resorting to a pathetic strawman.
- text: >-
my disappointment is immeasurable and my day is ruined. any idea if they
will ever fix it or is it just permanent? i feel like just wow man just
freaking wow
- text: >-
The stuff chatgpt gives is entirely too scripted *and* impractical, which
is what I'm trying to avoid :/
- text: >-
my experience with product product and brand: it's amazing and not a bit
scary. despite the articles about product's personality, my experience
shows the opposite: it's useful, friendly, and truly amazing technology.
- text: >-
product is a massive energy hog. have a bunch of tabs open and your
computer will come to a crawl. also, ad blocking is terrible on product
company ads) because product apparently has a "whitelist" of ads that it
refuses to be blocked. company is way better
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit 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: 0.5192307692307693
name: Accuracy
- type: f1
value:
- 0.2641509433962264
- 0.1553398058252427
- 0.6593406593406593
name: F1
- type: precision
value:
- 0.1590909090909091
- 0.09090909090909091
- 0.9375
name: Precision
- type: recall
value:
- 0.7777777777777778
- 0.5333333333333333
- 0.5084745762711864
name: Recall
SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
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.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: BAAI/bge-small-en-v1.5
- Classification head: a LogisticRegression instance
- 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 |
|---|---|
| peak |
|
| pit |
|
| neither |
|
Evaluation
Metrics
| Label | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|
| all | 0.5192 | [0.2641509433962264, 0.1553398058252427, 0.6593406593406593] | [0.1590909090909091, 0.09090909090909091, 0.9375] | [0.7777777777777778, 0.5333333333333333, 0.5084745762711864] |
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 SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("tjmooney98/725_test_model")
# Run inference
preds = model("The stuff chatgpt gives is entirely too scripted *and* impractical, which is what I'm trying to avoid :/")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 18 | 38.0667 | 91 |
| Label | Training Sample Count |
|---|---|
| pit | 5 |
| peak | 5 |
| neither | 5 |
Training Hyperparameters
- batch_size: (5, 5)
- num_epochs: (1, 1)
- 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 |
|---|---|---|---|
| 0.0333 | 1 | 0.1809 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.1
- PyTorch: 2.1.0+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}
}