Eguneratuta, bukatutzat eman genezake, emaitzak falta dira soilik

README.md

@@ -45,9 +45,11 @@ tags:
 
 This model achieves state-of-the-art performance on zero-shot Named Entity Recognition (NER) by first training on `GuideX`, a large-scale synthetic dataset with executable guidelines, and then fine-tuning on a collection of gold-standard IE datasets.
 
--   **Homepage:** [https://neilus03.github.io/guidex.com/](https://neilus03.github.io/guidex.com/)
--   **Paper:** [GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction](https://arxiv.org/abs/2506.00649)
--   **Code & Data:** The code and data for reproducing the GuideX methodology are available on the project homepage.
+- 💻 **Project Page:** [https://neilus03.github.io/guidex.com/](https://neilus03.github.io/guidex.com/)
+- 📒 **Code:** [GuideX codebase](https://github.com/Neilus03/GUIDEX)
+- 📖 **Paper:** [GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction](https://arxiv.org/abs/2506.00649)
+- 🐕 **GuideX Colection in the 🤗HuggingFace Hub:** [GuideX Collection](https://huggingface.co/collections/neildlf/guidex-6842ef478e8d9bb0a00c844d)
+- 🚀 **Example Jupyter Notebooks:** [GuideX Notebooks](https://github.com/Neilus03/GUIDEX/tree/dev-neil/notebooks)
 
 ## Model Description
 
@@ -58,3 +60,91 @@ This model achieves state-of-the-art performance on zero-shot Named Entity Recog
 -   **License:** Llama 2 Community License
 -   **Finetuned from model:** `meta-llama/Llama-3.1-8B`
 
+## Schema definition and inference example
+
+The labels are represented as Python classes, and the guidelines or instructions are introduced as docstrings. The model start generating after the `result = [` line.
+```Python
+# Entity definitions
+@dataclass
+class Launcher(Template):
+    """Refers to a vehicle designed primarily to transport payloads from the Earth's 
+    surface to space. Launchers can carry various payloads, including satellites, 
+    crewed spacecraft, and cargo, into various orbits or even beyond Earth's orbit. 
+    They are usually multi-stage vehicles that use rocket engines for propulsion."""
+
+    mention: str  
+    """
+    The name of the launcher vehicle. 
+    Such as: "Sturn V", "Atlas V", "Soyuz", "Ariane 5"
+    """
+    space_company: str # The company that operates the launcher. Such as: "Blue origin", "ESA", "Boeing", "ISRO", "Northrop Grumman", "Arianespace"
+    crew: List[str] # Names of the crew members boarding the Launcher. Such as: "Neil Armstrong", "Michael Collins", "Buzz Aldrin"
+    
+
+@dataclass
+class Mission(Template):
+    """Any planned or accomplished journey beyond Earth's atmosphere with specific objectives, 
+    either crewed or uncrewed. It includes missions to satellites, the International 
+    Space Station (ISS), other celestial bodies, and deep space."""
+    
+    mention: str
+    """
+    The name of the mission. 
+    Such as: "Apollo 11", "Artemis", "Mercury"
+    """
+    date: str # The start date of the mission
+    departure: str # The place from which the vehicle will be launched. Such as: "Florida", "Houston", "French Guiana"
+    destination: str # The place or planet to which the launcher will be sent. Such as "Moon", "low-orbit", "Saturn"
+
+# This is the text to analyze
+text = (
+    "The Ares 3 mission to Mars is scheduled for 2032. The Starship rocket build by SpaceX will take off from Boca Chica,"
+    "carrying the astronauts Max Rutherford, Elena Soto, and Jake Martinez."
+)
+
+# The annotation instances that take place in the text above are listed here
+result = [
+    Mission(mention='Ares 3', date='2032', departure='Boca Chica', destination='Mars'),
+    Launcher(mention='Starship', space_company='SpaceX', crew=['Max Rutherford', 'Elena Soto', 'Jake Martinez'])
+]
+```
+
+## How to Get Started with the Model
+
+Please read our [🚀 Example Jupyter Notebooks](https://github.com/Neilus03/GUIDEX/tree/dev-neil/notebooks) to get started with GuideX. 
+
+The best way to load the model is using our custom `load_model` fuction. However, you can also load them using the AutoModelForCausalLM class.
+
+**Important**: Our flash attention implementation has small numerical differences compared to the attention implementation in Huggingface.
+You must use the flag `trust_remote_code=True` or you will get inferior results. Flash attention requires an available CUDA GPU. Running GuideX
+pre-trained models on a CPU is not supported. We plan to address this in future releases. First, install flash attention 2:
+```bash
+pip install flash-attn --no-build-isolation
+pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
+```
+
+Then you can load the model using
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("HiTZ/Llama-3.1-GuideX-8B")
+model = AutoModelForCausalLM.from_pretrained("HiTZ/Llama-3.1-GuideX-8B", trust_remote_code=True, torch_dtype=torch.bfloat16)
+model.to("cuda")
+```
+
+Read our [🚀 Example Jupyter Notebooks](https://github.com/hitz-zentroa/GoLLIE/tree/main/notebooks) to learn how to easily define guidelines, generate model inputs and parse the output! 
+
+```
+## Citation
+@misc{delafuente2025guidexguidedsyntheticdata,
+      title={GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction}, 
+      author={Neil De La Fuente and Oscar Sainz and Iker García-Ferrero and Eneko Agirre},
+      year={2025},
+      eprint={2506.00649},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2506.00649}, 
+}
+```