--- license: apache-2.0 datasets: - MoritzLaurer/synthetic_zeroshot_mixtral_v0.1 - knowledgator/gliclass-v1.0 - fancyzhx/amazon_polarity - cnmoro/QuestionClassification - Arsive/toxicity_classification_jigsaw - shishir-dwi/News-Article-Categorization_IAB - SetFit/qnli - nyu-mll/multi_nli - SetFit/student-question-categories - SetFit/tweet_sentiment_extraction - SetFit/hate_speech18 - saattrupdan/doc-nli language: - en - fr - ge metrics: - f1 pipeline_tag: zero-shot-classification tags: - text classification - zero-shot - small language models - RAG - sentiment analysis base_model: - answerdotai/ModernBERT-large --- # ⭐ GLiClass: Generalist and Lightweight Model for Sequence Classification This is an efficient zero-shot classifier inspired by [GLiNER](https://github.com/urchade/GLiNER/tree/main) 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 and licensed data that allow commercial use 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. The backbone model is [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large), which effectively processes long sequences. ### How to use: First of all, you need to install GLiClass library: ```bash pip install gliclass pip install -U transformers>=4.48.0 ``` Than you need to initialize a model and a pipeline: ```python from gliclass import GLiClassModel, ZeroShotClassificationPipeline from transformers import AutoTokenizer model = GLiClassModel.from_pretrained("knowledgator/gliclass-modern-large-v2.0-init") tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-modern-large-v2.0-init") 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"]) ``` If you want to use it for NLI type of tasks, we recommend representing your premise as a text and hypothesis as a label, you can put several hypotheses, but the model works best with a single input hypothesis. ```python # Initialize model and multi-label pipeline text = "The cat slept on the windowsill all afternoon" labels = ["The cat was awake and playing outside."] results = pipeline(text, labels, threshold=0.0)[0] print(results) ``` ### 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-modern-large-v2.0-init (399 M)](knowledgator/gliclass-modern-large-v2.0-init) | 0.9137 | 0.7357 | 0.4140 | | [gliclass-modern-base-v2.0-init (151 M)](knowledgator/gliclass-modern-base-v2.0-init) | 0.8264 | 0.6637 | 0.2985 | | [gliclass-large-v1.0 (438 M)](https://huggingface.co/knowledgator/gliclass-large-v1.0) | 0.9404 | 0.7516 | 0.4874 | | [gliclass-base-v1.0 (186 M)](https://huggingface.co/knowledgator/gliclass-base-v1.0) | 0.8650 | 0.6837 | 0.4749 | | [gliclass-small-v1.0 (144 M)](https://huggingface.co/knowledgator/gliclass-small-v1.0) | 0.8650 | 0.6805 | 0.4664 | | [Bart-large-mnli (407 M)](https://huggingface.co/facebook/bart-large-mnli) | 0.89 | 0.6887 | 0.3765 | | [Deberta-base-v3 (184 M)](https://huggingface.co/cross-encoder/nli-deberta-v3-base) | 0.85 | 0.6455 | 0.5095 | | [Comprehendo (184M)](https://huggingface.co/knowledgator/comprehend_it-base) | 0.90 | 0.7982 | 0.5660 | | SetFit [BAAI/bge-small-en-v1.5 (33.4M)](https://huggingface.co/BAAI/bge-small-en-v1.5) | 0.86 | 0.5636 | 0.5754 | Below you can find a comparison with other GLiClass models: | Dataset | gliclass-base-v1.0-init | gliclass-large-v1.0-init | gliclass-modern-base-v2.0-init | gliclass-modern-large-v2.0-init | |----------------------|-----------------------|-----------------------|---------------------|---------------------| | CR | 0.8672 | 0.8024 | 0.9041 | 0.8980 | | sst2 | 0.8342 | 0.8734 | 0.9011 | 0.9434 | | sst5 | 0.2048 | 0.1638 | 0.1972 | 0.1123 | | 20_news_groups | 0.2317 | 0.4151 | 0.2448 | 0.2792 | | spam | 0.5963 | 0.5407 | 0.5074 | 0.6364 | | financial_phrasebank | 0.3594 | 0.3705 | 0.2537 | 0.2562 | | imdb | 0.8772 | 0.8836 | 0.8255 | 0.9137 | | ag_news | 0.5614 | 0.7069 | 0.6050 | 0.6933 | | emotion | 0.2865 | 0.3840 | 0.2474 | 0.3746 | | cap_sotu | 0.3966 | 0.4353 | 0.2929 | 0.2919 | | rotten_tomatoes | 0.6626 | 0.7933 | 0.6630 | 0.5928 | | **AVERAGE:** | 0.5344 | 0.5790 | 0.5129 | 0.5447 | Here you can see how the performance of the model grows providing more examples: | Model | Num Examples | sst5 | ag_news | emotion | **AVERAGE:** | |------------------------------------|------------------|--------|---------|--------------|----------| | gliclass-modern-large-v2.0-init | 0 | 0.1123 | 0.6933 | 0.3746 | 0.3934 | | gliclass-modern-large-v2.0-init | 8 | 0.5098 | 0.8339 | 0.5010 | 0.6149 | | gliclass-modern-large-v2.0-init | Weak Supervision | 0.0951 | 0.6478 | 0.4520 | 0.3983 | | gliclass-modern-base-v2.0-init | 0 | 0.1972 | 0.6050 | 0.2474 | 0.3499 | | gliclass-modern-base-v2.0-init | 8 | 0.3604 | 0.7481 | 0.4420 | 0.5168 | | gliclass-modern-base-v2.0-init | Weak Supervision | 0.1599 | 0.5713 | 0.3216 | 0.3509 | | gliclass-large-v1.0-init | 0 | 0.1639 | 0.7069 | 0.3840 | 0.4183 | | gliclass-large-v1.0-init | 8 | 0.4226 | 0.8415 | 0.4886 | 0.5842 | | gliclass-large-v1.0-init | Weak Supervision | 0.1689 | 0.7051 | 0.4586 | 0.4442 | | gliclass-base-v1.0-init | 0 | 0.2048 | 0.5614 | 0.2865 | 0.3509 | | gliclass-base-v1.0-init | 8 | 0.2007 | 0.8359 | 0.4856 | 0.5074 | | gliclass-base-v1.0-init | Weak Supervision | 0.0681 | 0.6627 | 0.3066 | 0.3458 |