Update README.md

README.md

@@ -1,8 +1,90 @@
 ---
 library_name: peft
 base_model: meta-llama/Meta-Llama-3-8B-Instruct
+license: apache-2.0
+language:
+- en
 ---
-## Training procedure
+## Overview
+
+The model is a LoRa Adaptor based on LLaMA-3-8B-Instruct. The model has been trained on a [re-annotated version](https://github.com/Teddy-Li/MulVOIEL/tree/master/CaRB/data) of the [CaRB dataset](https://github.com/dair-iitd/CaRB).
+
+The model produces multi-valent Open IE tuples, i.e. relations with various numbers of arguments (1, 2, or more). We provide an example below:
+
+Consider the following sentence (taken from the CaRB dev set):
+
+`Earlier this year , President Bush made a final `` take - it - or - leave it '' offer on the minimum wage`
+
+Our model would extract the following relation from the sentence:
+
+ <<span style="color:#2471A3">President Bush</span>, <span style="color:#A93226">made</span>, <span style="color:#138D75">a final "take-it-or-leave-it" offer</span>, <span style="color:#B7950B ">on the minimum wage</span>, <span style="color:#B9770E">earlier this year</span>>
+
+where we include <span style="color:#2471A3">President Bush</span> as the <span style="color:#2471A3">subject</span>, <span style="color:#A93226">made</span> as the <span style="color:#A93226">object</span>, <span style="color:#138D75">a final "take-it-or-leave-it" offer</span> as the<span style="color:#138D75">direct object</span>, and <span style="color:#B7950B ">on the minimum wage</span> and <span style="color:#B9770E">earlier this year</span>> as salient <span style="color:#B7950B">_compl_</span><span style="color:#B9770E">_ements_</span>.
+
+We briefly describe how to use our model in the below, and provide further details in our [MulVOIEL repository on Github](https://github.com/Teddy-Li/MulVOIEL/)
+
+
+## Getting Started
+
+### Model Output Format
+
+Given a sentence, the model produces textual predictions in the following format:
+
+`<subj> ,, (<auxi> ###) <predicate> ,, (<prep1> ###) <obj1>, (<prep2> ###) <obj2>, ...`
+
+### How to Use
+
+1. Install the relevant libraries (using the LLaMA3-8b-instruct model as an example):
+    ```bash
+    pip install transformers datasets peft torch
+    ```
+
+2. Load the model and perform inference (example):
+    ```python
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from peft import PeftModel
+    import torch
+    from llamaOIE import parse_outstr_to_triples
+    from llamaOIE_dataset import prepare_input
+
+    base_model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
+    peft_adapter_name = "Teddy487/LLaMA3-8b-for-OpenIE"
+
+    model = AutoModelForCausalLM.from_pretrained(base_model_name)
+    model = PeftModel.from_pretrained(model, peft_adapter_name)
+    tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+    input_text = "Earlier this year , President Bush made a final `` take - it - or - leave it '' offer on the minimum wage"
+    input_text, _ = prepare_input({'s': input_text}, tokenizer, has_labels=False)
+
+    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
+
+    outputs = model.generate(input_ids)
+    outstr = tokenizer.decode(outputs[0][len(input_ids):], skip_special_tokens=True)
+    triples = parse_outstr_to_triples(outstr)
+
+    for tpl in triples:
+        print(tpl)
+    ```
+
+    🍺
+
+## Model Performance
+
+The primary benefit of our model is the ability to extract finer-grained information for predicates. On the other hand, we also report performance on a roughly comparable basis with prior SOTA open IE models, where our method is comparable and even superior to prior models, while producing finer-grained and more complex outputs. We report evaluation results in (macro) F-1 metric, as well as in the average [Levenshtein Distance](https://pypi.org/project/python-Levenshtein/) between gold and predicted relations:
+
+| Model | Levenshtein Distance | Macro F-1 |
+| --- | --- | --- | --- |
+| [LoRA LLaMA2-7b](https://huggingface.co/Teddy487/LLaMA2-7b-for-OpenIE) | 5.85 | 50.21% |
+| [LoRA LLaMA3-8b](https://huggingface.co/Teddy487/LLaMA3-8b-for-OpenIE) | 5.04 | 55.32% |
+| RNN OIE * | - | 49.0 |
+| IMOJIE * | - | 53.5 |
+| Open IE 6 * | - | 54.0/52.7 |
+
+Note that the precision and recall values are not directly comparable, because we evaluate the model prediction at a finer granularity, and we use different train/dev/test arrangements as the original CaRB dataset, hence the asterisk.
+
+
+
 
 ### Framework versions