Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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](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.
+
+This version of the model utilize the [LLM2Vec](https://github.com/McGill-NLP/llm2vec/tree/main/llm2vec) approach for converting modern decoders to bi-directional encoder. It brings the following benefits:
+* Enhanced performance and generalization capabilities;
+* Support for Flash Attention;
+* Extended context window.
+
+
+### 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-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:
+While the model is some how comparable to DeBERTa version in zero-shot setting, it demonstrates state-of-the-art performance in few-shot setting.
+![Few-shot performance](few_shot.png)
+
+### Join Our Discord
+
+Connect with our community on Discord for news, support, and discussion about our models. Join [Discord](https://discord.gg/dkyeAgs9DG).