Hugging Face's logo

knowledgator
/
gliclass-large-v1.0-lw

Zero-Shot Classification
Transformers
ONNX
Safetensors
English
GLiClass
text classification
zero-shot
small language models
RAG
sentiment analysis
Inference Endpoints
Model card Files Community
gliclass-large-v1.0-lw / README.md
Ihor's picture
Ihor
Update README.md
01d1f8f verified
preview code
|
raw
history blame
7.53 kB
metadata
license: apache-2.0
datasets:
  - MoritzLaurer/synthetic_zeroshot_mixtral_v0.1
language:
  - en
metrics:
  - f1
pipeline_tag: zero-shot-classification
tags:
  - text classification
  - zero-shot
  - small language models
  - RAG
  - sentiment analysis

⭐ 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.

This version of the model uses a layer-wise selection of features that enables a better understanding of different levels of language.

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

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