Edit model card

⭐ GLiClass: Generalist and Lightweight Model for Sequence Classification

This is an efficient zero-shot classifier inspired by GLiNER work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path.

It can be used for topic classification, sentiment analysis and as a reranker in RAG pipelines.

The model was trained on synthetic data and can be used in commercial applications.

How to use:

First of all, you need to install GLiClass library:

pip install gliclass

Than you need to initialize a model and a pipeline:

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

model = GLiClassModel.from_pretrained("knowledgator/gliclass-large-v1.0")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-large-v1.0")

pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')

text = "One day I will see the world!"
labels = ["travel", "dreams", "sport", "science", "politics"]
results = pipeline(text, labels, threshold=0.5)[0] #because we have one text

for result in results:
 print(result["label"], "=>", result["score"])

Benchmarks:

Below, you can see the F1 score on several text classification datasets. All tested models were not fine-tuned on those datasets and were tested in a zero-shot setting.

Model IMDB AG_NEWS Emotions
gliclass-large-v1.0 (438 M) 0.9404 0.7516 0.4874
gliclass-base-v1.0 (186 M) 0.8650 0.6837 0.4749
gliclass-small-v1.0 (144 M) 0.8650 0.6805 0.4664
Bart-large-mnli (407 M) 0.89 0.6887 0.3765
Deberta-base-v3 (184 M) 0.85 0.6455 0.5095
Comprehendo (184M) 0.90 0.7982 0.5660
SetFit BAAI/bge-small-en-v1.5 (33.4M) 0.86 0.5636 0.5754

Below you can find a comparison with other GLiClass models:

Dataset gliclass-small-v1.0-lw gliclass-base-v1.0-lw gliclass-large-v1.0-lw gliclass-small-v1.0 gliclass-base-v1.0 gliclass-large-v1.0
CR 0.8886 0.9097 0.9226 0.8824 0.8942 0.9219
sst2 0.8392 0.8987 0.9247 0.8518 0.8979 0.9269
sst5 0.2865 0.3779 0.2891 0.2424 0.2789 0.3900
20_news_groups 0.4572 0.3953 0.4083 0.3366 0.3576 0.3863
spam 0.5118 0.5126 0.3642 0.4089 0.4938 0.3661
rotten_tomatoes 0.8015 0.8429 0.8807 0.7987 0.8508 0.8808
massive 0.3180 0.4635 0.5606 0.2546 0.1893 0.4376
banking 0.1768 0.4396 0.3317 0.1374 0.2077 0.2847
yahoo_topics 0.4686 0.4784 0.4760 0.4477 0.4516 0.4921
financial_phrasebank 0.8665 0.8880 0.9044 0.8901 0.8955 0.8735
imdb 0.9048 0.9351 0.9429 0.8982 0.9238 0.9333
ag_news 0.7252 0.6985 0.7559 0.7242 0.6848 0.7503
dair_emotion 0.4012 0.3516 0.3951 0.3450 0.2357 0.4013
capsotu 0.3794 0.4643 0.4749 0.3432 0.4375 0.4644
Average: 0.5732 0.6183 0.6165 0.5401 0.5571 0.6078

Here you can see how the performance of the model grows providing more examples:

Model Num Examples sst5 spam massive banking ag news dair emotion capsotu Average
gliclass-small-v1.0-lw 0 0.2865 0.5118 0.318 0.1768 0.7252 0.4012 0.3794 0.3998428571
gliclass-base-v1.0-lw 0 0.3779 0.5126 0.4635 0.4396 0.6985 0.3516 0.4643 0.4725714286
gliclass-large-v1.0-lw 0 0.2891 0.3642 0.5606 0.3317 0.7559 0.3951 0.4749 0.4530714286
gliclass-small-v1.0 0 0.2424 0.4089 0.2546 0.1374 0.7242 0.345 0.3432 0.3508142857
gliclass-base-v1.0 0 0.2789 0.4938 0.1893 0.2077 0.6848 0.2357 0.4375 0.3611
gliclass-large-v1.0 0 0.39 0.3661 0.4376 0.2847 0.7503 0.4013 0.4644 0.4420571429
gliclass-small-v1.0-lw 8 0.2709 0.84026 0.62 0.6883 0.7786 0.449 0.4918 0.5912657143
gliclass-base-v1.0-lw 8 0.4275 0.8836 0.729 0.7667 0.7968 0.3866 0.4858 0.6394285714
gliclass-large-v1.0-lw 8 0.3345 0.8997 0.7658 0.848 0.84843 0.5219 0.508 0.67519
gliclass-small-v1.0 8 0.3042 0.5683 0.6332 0.7072 0.759 0.4509 0.4434 0.5523142857
gliclass-base-v1.0 8 0.3387 0.7361 0.7059 0.7456 0.7896 0.4323 0.4802 0.6040571429
gliclass-large-v1.0 8 0.4365 0.9018 0.77 0.8533 0.8509 0.5061 0.4935 0.6874428571
Downloads last month
310
Safetensors
Model size
438M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for knowledgator/gliclass-large-v1.0

Finetunes
1 model

Dataset used to train knowledgator/gliclass-large-v1.0

Space using knowledgator/gliclass-large-v1.0 1

Collection including knowledgator/gliclass-large-v1.0