Update README.md

README.md

@@ -1,3 +1,59 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- MoritzLaurer/synthetic_zeroshot_mixtral_v0.1
+language:
+- en
+metrics:
+- f1
+pipeline_tag: text-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](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 data and can be used in commercial applications.
+
+### How to use:
+First of all, you need to install GLiClass library:
+```bash
+pip install gliclass
+```
+
+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-base-v1")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-base-v1")
+
+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-base-v1.0 (144 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 |