Update README.md

README.md

@@ -1,199 +1,103 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- Tevatron/msmarco-passage
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-### Model Sources [optional]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Metrics
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+# QWEN2.5 based Setwise reranker fine-tuned on MSMARCO dataset
+
+GitHub repo: https://github.com/ielab/llm-rankers/tree/main/Rank-R1
+
+# Python code examples:
+
+Using [llm-rankers](https://github.com/ielab/llm-rankers) library:
+```Python
+from llmrankers.setwise import RankR1SetwiseLlmRanker
+from llmrankers.rankers import SearchResult
+
+docs = [SearchResult(docid=i, text=f'this is passage {i}', score=None) for i in range(20)]
+query = 'Give me passage 6'
+
+ranker = RankR1SetwiseLlmRanker(
+    model_name_or_path='Qwen/Qwen2.5-3B-Instruct',
+    lora_name_or_path='ielabgroup/Setwise-SFT-3B-v0.1',
+    prompt_file='prompt_setwise.toml',
+    num_child=19,
+    k=1,
+    verbose=True
+)
+
+print(ranker.rerank(query, docs)[0])
+```
+
+The `prompt_setwise.toml` is a .toml file with the following fields:
+
+```toml
+prompt_system = "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant provides the user with the answer enclosed within <answer> </answer> tags, i.e., <answer> answer here </answer>."
+
+prompt_user = '''Given the query: "{query}", which of the following documents is most relevant?
+{docs}
+Please provide only the label of the most relevant document to the query, enclosed in square brackets, within the answer tags. For example, if the third document is the most relevant, the answer should be: <answer>[3]</answer>.'''
+
+pattern = '<answer>(.*?)</answer>'
+```
+
+Internally, the above code is equivalent to the following transformers code:
+```Python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+def get_model(peft_model_name):
+    config = PeftConfig.from_pretrained(peft_model_name)
+    base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+    model = PeftModel.from_pretrained(base_model, peft_model_name)
+    model = model.merge_and_unload()
+    return model
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen2.5-3B-Instruct')
+model = get_model('ielabgroup/Setwise-SFT-3B-v0.1').to('cuda:0').eval()
+
+prompt_system = "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant provides the user with the answer enclosed within <answer> </answer> tags, i.e., <answer> answer here </answer>."
+prompt_user = '''Given the query: "{query}", which of the following documents is most relevant?
+{docs}
+Please provide only the label of the most relevant document to the query, enclosed in square brackets, within the answer tags. For example, if the third document is the most relevant, the answer should be: <answer>[3]</answer>.'''
+
+query = 'Give me passage 6'
+docs = [f'[{i+1}] this is passage {i+1}' for i in range(20)]
+docs = '\n'.join(docs)
+
+messages = [
+    {'role': "system", 'content': prompt_system},
+    {'role': "user", 'content': prompt_user.format(query=query, docs=docs)}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to('cuda:0')
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=2048,
+    do_sample=False,
+)
+generated_ids = [
+    output_ids[len(input_ids)-1:] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+'''
+<answer>[6]</answer>
+'''
+
+# extract the answer
+import re
+pattern = '<answer>(.*?)</answer>'
+answer = re.search(pattern, response, re.DOTALL).group(1) # answer = '[6]'
+```
+> Note that this Setwise rerankers are trained with the prompt format shown above, which includes 20 documents. Other numbers of documents should also work fine, but this would represent a "zero-shot" setting for the model.