Text Classification
Transformers
Safetensors
PEFT
English
code
qwen3
text-generation
code-search
reranker
code-retrieval
lora
text-embeddings-inference
Instructions to use hq-bench/coreb-code-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hq-bench/coreb-code-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hq-bench/coreb-code-reranker")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hq-bench/coreb-code-reranker") model = AutoModelForCausalLM.from_pretrained("hq-bench/coreb-code-reranker") - PEFT
How to use hq-bench/coreb-code-reranker with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 741 Bytes
caad463 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {%- set instruction = messages | selectattr("role", "eq", "system") | map(attribute="content") | first | default("Given a web search query, retrieve relevant passages that answer the query") -%}
{%- set query_text = messages | selectattr("role", "eq", "query") | map(attribute="content") | first -%}
{%- set document_text = messages | selectattr("role", "eq", "document") | map(attribute="content") | first -%}
<|im_start|>system
Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>
<|im_start|>user
<Instruct>: {{ instruction }}
<Query>: {{ query_text }}
<Document>: {{ document_text }}<|im_end|>
<|im_start|>assistant
<think>
</think>
|