Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- BioMike/formal-logic-reasoning-gliclass-2k
+- knowledgator/gliclass-v3-logic-dataset
+- tau/commonsense_qa
+metrics:
+- f1
+tags:
+- text classification
+- nli
+- sentiment analysis
+pipeline_tag: text-classification
+---
+
+![image/png](instruct.png)
+
+# GLiClass-multitask: Efficient zero-shot and few-shot multi-task model via sequence classification
+
+GLiClass is an efficient zero-shot sequence classification model designed to achieve SoTA performance while being much faster than cross-encoders and LLMs, while preserving strong generalization capabilities.
+
+The model supports text classification with any labels and can be used for the following tasks:
+* Topic Classification
+* Sentiment Analysis
+* Intent Classification
+* Reranking
+* Hallucination Detection
+* Rule-following Verification
+* LLM-safety Classification
+* Natural Language Inference
+
+## ✨ What's New in V3
+
+- **Hierarchical Labels** — Organize labels into groups using dot notation or dictionaries (e.g., `sentiment.positive`, `topic.product`).
+- **Few-Shot Examples** — Provide in-context examples to boost accuracy on your specific task.
+- **Label Descriptions** — Add natural-language descriptions to labels for more precise classification.
+- **Task Prompts** — Prepend a custom prompt to guide the model's classification behavior.
+
+See the [GLiClass library README](https://github.com/Knowledgator/GLiClass) for full details on these features.
+
+## Installation
+
+```bash
+pip install gliclass
+```
+
+## Quick Start
+
+```python
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+from transformers import AutoTokenizer
+
+model = GLiClassModel.from_pretrained("knowledgator/gliclass-instruct-base-v3.0")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-instruct-base-v3.0")
+pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
+```
+
+---
+
+## Task Examples
+
+### 1. Topic Classification
+
+```python
+text = "NASA launched a new Mars rover to search for signs of ancient life."
+labels = ["space", "politics", "sports", "technology", "health"]
+
+results = pipeline(text, labels, threshold=0.5)[0]
+for r in results:
+    print(r["label"], "=>", r["score"])
+```
+
+#### With hierarchical labels
+
+```python
+hierarchical_labels = {
+    "science": ["space", "biology", "physics"],
+    "society": ["politics", "economics", "culture"]
+}
+
+results = pipeline(text, hierarchical_labels, threshold=0.5)[0]
+for r in results:
+    print(r["label"], "=>", r["score"])
+# e.g. science.space => 0.95
+```
+
+### 2. Sentiment Analysis
+
+```python
+text = "The food was excellent but the service was painfully slow."
+labels = ["positive", "negative", "neutral"]
+
+results = pipeline(text, labels, threshold=0.5)[0]
+for r in results:
+    print(r["label"], "=>", r["score"])
+```
+
+#### With a task prompt
+
+```python
+results = pipeline(
+    text, labels,
+    prompt="Classify the sentiment of this restaurant review:",
+    threshold=0.5
+)[0]
+```
+
+### 3. Intent Classification
+
+```python
+text = "Can you set an alarm for 7am tomorrow?"
+labels = ["set_alarm", "play_music", "get_weather", "send_message", "set_reminder"]
+
+results = pipeline(text, labels, threshold=0.5)[0]
+for r in results:
+    print(r["label"], "=>", r["score"])
+```
+
+#### With few-shot examples
+
+```python
+examples = [
+    {"text": "Wake me up at 6:30.", "labels": ["set_alarm"]},
+    {"text": "Play some jazz.", "labels": ["play_music"]},
+]
+
+results = pipeline(text, labels, examples=examples, threshold=0.5)[0]
+for r in results:
+    print(r["label"], "=>", r["score"])
+```
+
+### 4. Natural Language Inference
+
+Represent your premise as the text and the hypothesis as a label. The model works best with a single hypothesis at a time.
+
+```python
+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)
+# Low score → contradiction
+```
+
+### 5. Reranking
+
+Score query–passage relevance by treating passages as texts and the query as the label:
+
+```python
+query = "How to train a neural network?"
+passages = [
+    "Backpropagation is the key algorithm for training deep neural networks.",
+    "The stock market rallied on strong earnings reports.",
+    "Gradient descent optimizes model weights during training.",
+]
+
+for passage in passages:
+    score = pipeline(passage, [query], threshold=0.0)[0][0]["score"]
+    print(f"{score:.3f}  {passage[:60]}")
+```
+
+### 6. Hallucination Detection
+
+Concatenate context, question, and answer into the text field:
+
+```python
+text = (
+    "Context: The Eiffel Tower was built from 1887 to 1889 and is 330 m tall. "
+    "It was the tallest structure until the Chrysler Building in 1930.\n"
+    "Question: When was the Eiffel Tower built and how tall is it?\n"
+    "Answer: It was built 1887–1889, stands 330 m tall, and was the tallest "
+    "structure until the Empire State Building in 1931."
+)
+labels = ["hallucinated", "correct"]
+
+results = pipeline(text, labels, threshold=0.0)[0]
+for r in results:
+    print(r["label"], "=>", r["score"])
+# "hallucinated" should score higher (Empire State Building & 1931 are wrong)
+```
+
+### 7. Rule-following Verification
+
+Include the domain and rules as part of the text:
+
+```python
+text = (
+    "Domain: e-commerce product reviews\n"
+    "Rule: No promotion of illegal activity.\n"
+    "Text: The software is okay, but search for 'productname_patch_v2.zip' "
+    "to unlock all features for free."
+)
+labels = ["follows_guidelines", "violates_guidelines"]
+
+results = pipeline(text, labels, threshold=0.0)[0]
+for r in results:
+    print(r["label"], "=>", r["score"])
+```
+
+### 8. LLM-safety Classification
+
+```python
+text = "I'm looking for a good Italian restaurant near downtown Chicago, budget ~$50/person."
+labels = [
+    "benign request",
+    "prompt injection",
+    "system prompt extraction",
+    "jailbreak attempt",
+    "harmful content request",
+    "social engineering",
+    "data exfiltration",
+]
+
+results = pipeline(text, labels, threshold=0.5)[0]
+for r in results:
+    print(r["label"], "=>", r["score"])
+```
+
+---
+
+## Benchmarks
+
+F1 scores on zero-shot text classification (no fine-tuning on these datasets):
+
+GLiClass-V1 Multitask:
+
+| Dataset | [large‑v1.0](https://huggingface.co/knowledgator/gliclass-instruct-large-v1.0) | [base‑v1.0](https://huggingface.co/knowledgator/gliclass-instruct-base-v1.0) | [edge‑v1.0](https://huggingface.co/knowledgator/gliclass-instruct-edge-v1.0) |
+|---|---|---|---|
+| CR | 0.9066 | 0.8922 | 0.7933 |
+| sst2 | 0.9154 | 0.9198 | 0.7577 |
+| sst5 | 0.3387 | 0.2266 | 0.2163 |
+| 20_newsgroups | 0.5577 | 0.5189 | 0.2555 |
+| spam | 0.9790 | 0.9380 | 0.7609 |
+| financial_phrasebank | 0.8289 | 0.5217 | 0.3905 |
+| imdb | 0.9397 | 0.9364 | 0.8159 |
+| ag_news | 0.7521 | 0.6978 | 0.6043 |
+| emotion | 0.4473 | 0.4454 | 0.2941 |
+| cap_sotu | 0.4327 | 0.4579 | 0.2380 |
+| rotten_tomatoes | 0.8491 | 0.8458 | 0.5455 |
+| massive | 0.5824 | 0.4757 | 0.2090 |
+| banking | 0.6987 | 0.6072 | 0.4635 |
+| snips | 0.8509 | 0.6515 | 0.5461 |
+| **AVERAGE** | **0.7199** | **0.6525** | **0.4922** |
+
+GLiClass-V3:
+
+| Dataset | [large‑v3.0](https://huggingface.co/knowledgator/gliclass-large-v3.0) | [base‑v3.0](https://huggingface.co/knowledgator/gliclass-base-v3.0) | [modern‑large‑v3.0](https://huggingface.co/knowledgator/gliclass-modern-large-v3.0) | [modern‑base‑v3.0](https://huggingface.co/knowledgator/gliclass-modern-base-v3.0) | [edge‑v3.0](https://huggingface.co/knowledgator/gliclass-edge-v3.0) |
+|---|---|---|---|---|---|
+| CR | 0.9398 | 0.9127 | 0.8952 | 0.8902 | 0.8215 |
+| sst2 | 0.9192 | 0.8959 | 0.9330 | 0.8959 | 0.8199 |
+| sst5 | 0.4606 | 0.3376 | 0.4619 | 0.2756 | 0.2823 |
+| 20_newsgroups | 0.5958 | 0.4759 | 0.3905 | 0.3433 | 0.2217 |
+| spam | 0.7584 | 0.6760 | 0.5813 | 0.6398 | 0.5623 |
+| financial_phrasebank | 0.9000 | 0.8971 | 0.5929 | 0.4200 | 0.5004 |
+| imdb | 0.9366 | 0.9251 | 0.9402 | 0.9158 | 0.8485 |
+| ag_news | 0.7181 | 0.7279 | 0.7269 | 0.6663 | 0.6645 |
+| emotion | 0.4506 | 0.4447 | 0.4517 | 0.4254 | 0.3851 |
+| cap_sotu | 0.4589 | 0.4614 | 0.4072 | 0.3625 | 0.2583 |
+| rotten_tomatoes | 0.8411 | 0.7943 | 0.7664 | 0.7070 | 0.7024 |
+| massive | 0.5649 | 0.5040 | 0.3905 | 0.3442 | 0.2414 |
+| banking | 0.5574 | 0.4698 | 0.3683 | 0.3561 | 0.0272 |
+| snips | 0.9692 | 0.9474 | 0.7707 | 0.5663 | 0.5257 |
+| **AVERAGE** | **0.7193** | **0.6764** | **0.6197** | **0.5577** | **0.4900** |
+
+## Citation
+
+```bibtex
+@misc{stepanov2025gliclassgeneralistlightweightmodel,
+      title={GLiClass: Generalist Lightweight Model for Sequence Classification Tasks}, 
+      author={Ihor Stepanov and Mykhailo Shtopko and Dmytro Vodianytskyi and Oleksandr Lukashov and Alexander Yavorskyi and Mykyta Yaroshenko},
+      year={2025},
+      eprint={2508.07662},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2508.07662}, 
+}
+```