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gliclass-x-base

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GLiClass

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alexandrlukashov commited on Jul 22, 2025

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: apache-2.0
+datasets:
+- knowledgator/gliclass-v2.0
 ---
+# ⭐ 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.
+The backbone model is [mdeberta-v3-base](huggingface.co/microsoft/mdeberta-v3-base). It supports multilingual understanding, making it well-suited for tasks involving texts in different languages.
+### 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:
+<details>
+<summary>English</summary>
+```python
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+from transformers import AutoTokenizer
+model = GLiClassModel.from_pretrained("knowledgator/gliclass-x-base")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-x-base", add_prefix_space=True)
+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"])
+```
+</details>
+<details>
+<summary>Spanish</summary>
+```python
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+from transformers import AutoTokenizer
+model = GLiClassModel.from_pretrained("knowledgator/gliclass-x-base")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-x-base", add_prefix_space=True)
+pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
+text = "¡Un día veré el mundo!"
+labels = ["viajes", "sueños", "deportes", "ciencia", "política"]
+results = pipeline(text, labels, threshold=0.5)[0]
+for result in results:
+    print(result["label"], "=>", result["score"])
+```
+</details>
+<details>
+<summary>Italitan</summary>
+```python
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+from transformers import AutoTokenizer
+model = GLiClassModel.from_pretrained("knowledgator/gliclass-x-base")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-x-base", add_prefix_space=True)
+pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
+text = "Un giorno vedrò il mondo!"
+labels = ["viaggi", "sogni", "sport", "scienza", "politica"]
+results = pipeline(text, labels, threshold=0.5)[0]
+for result in results:
+    print(result["label"], "=>", result["score"])
+```
+</details>
+<details>
+<summary>French</summary>
+```python
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+from transformers import AutoTokenizer
+model = GLiClassModel.from_pretrained("knowledgator/gliclass-x-base")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-x-base", add_prefix_space=True)
+pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
+text = "Un jour, je verrai le monde!"
+labels = ["voyage", "rêves", "sport", "science", "politique"]
+results = pipeline(text, labels, threshold=0.5)[0]
+for result in results:
+    print(result["label"], "=>", result["score"])
+```
+</details>
+<details>
+<summary>German</summary>
+```python
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+from transformers import AutoTokenizer
+model = GLiClassModel.from_pretrained("knowledgator/gliclass-x-base")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-x-base", add_prefix_space=True)
+pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
+text = "Eines Tages werde ich die Welt sehen!"
+labels = ["Reisen", "Träume", "Sport", "Wissenschaft", "Politik"]
+results = pipeline(text, labels, threshold=0.5)[0]
+for result in results:
+    print(result["label"], "=>", result["score"])
+```
+</details>
+### 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.
+#### Multilingual benchmarks
+| Dataset                  | knowledgator/gliclass-x-base | knowledgator/gliclass-base-v3.0 | knowledgator/gliclass-large-v3.0 |
+|--------------------------|------------------------------|---------------------------------|----------------------------------|
+| FredZhang7/toxi-text-3M  | 0.5972                       | 0.5072                          | 0.6118                           |
+| SetFit/xglue_nc          | 0.5014                       | 0.5348                          | 0.5378                           |
+| Davlan/sib200_14classes  | 0.4663                       | 0.2867                          | 0.3173                           |
+| uhhlt/GermEval2017       | 0.3999                       | 0.4010                          | 0.4299                           |
+| dolfsai/toxic_es         | 0.1250                       | 0.1399                          | 0.1412                           |
+#### General benchmarks
+| Dataset                         | gliclass-x-base | gliclass-base-v3.0 | gliclass-large-v3.0 |
+|--------------------------------|-----------------|--------------------|---------------------|
+| SetFit/CR                      | 0.8630          | 0.9398             | 0.9400              |
+| SetFit/sst2                   | 0.8554          | 0.9192             | 0.9192              |
+| SetFit/sst5                   | 0.3287          | 0.4606             | 0.4606              |
+| AmazonScience/massive          | 0.2611          | 0.5649             | 0.5650              |
+| stanfordnlp/imdb              | 0.8840          | 0.9366             | 0.9366              |
+| SetFit/20_newsgroups          | 0.4116          | 0.5958             | 0.5958              |
+| SetFit/enron_spam             | 0.5929          | 0.7584             | 0.7584              |
+| PolyAI/banking77              | 0.3098          | 0.5574             | 0.5574              |
+| takala/financial_phrasebank   | 0.7851          | 0.9000             | 0.9000              |
+| ag_news                      | 0.6815          | 0.7181             | 0.7181              |
+| dair-ai/emotion               | 0.3667          | 0.4506             | 0.4510              |
+| MoritzLaurer/cap_sotu         | 0.3935          | 0.4589             | 0.6118              |
+| cornell-movie-review-data/rotten_tomatoes | 0.8411  | 0.8411             | 0.8411              |