Improve language tag

README.md

@@ -1,184 +1,196 @@
----
-base_model:
-- Qwen/Qwen2.5-32B
-datasets:
-- jhu-clsp/rank1-training-data
-language:
-- en
-license: mit
-pipeline_tag: text-ranking
-library_name: transformers
-tags:
-- reranker
-- retrieval
----
-
-# rank1-32b: Test-Time Compute for Reranking in Information Retrieval
-
-📄 [Paper](https://arxiv.org/abs/2502.18418) | 🚀 [GitHub Repository](https://github.com/orionw/rank1)
-
-rank1 is a reasoning reranker model that "thinks" before making relevance judgments. This 32B parameter model is trained from the Qwen2.5-32B base model and leverages test-time compute to generate reasoning chains before deciding if a document is relevant to a query.
-
-## Model Description
-
-rank1 introduces a novel approach to information retrieval by generating explicit reasoning chains before making relevance judgments. Unlike traditional rerankers that directly output scores, rank1:
-
-1. Receives a query and document pair
-2. Generates a reasoning chain within a `<think>...</think>` section
-3. Makes a binary relevance judgment (`true` or `false`)
-4. Returns a confidence score based on the logits of the true/false tokens
-
-This approach helps the model break down complex relevance decisions into logical steps, improving performance across diverse retrieval tasks.
-
-## Model Family
-
-| Model | Base | Description |
-|:------|:-----|:------------|
-| [rank1-0.5b](https://huggingface.co/jhu-clsp/rank1-0.5b) | Qwen2.5-0.5B | Smallest variant (0.5B parameters) |
-| [rank1-1.5b](https://huggingface.co/jhu-clsp/rank1-1.5b) | Qwen2.5-1.5B | Smaller variant (1.5B parameters) |
-| [rank1-3b](https://huggingface.co/jhu-clsp/rank1-3b) | Qwen2.5-3B | Smaller variant (3B parameters) |
-| [rank1-7b](https://huggingface.co/jhu-clsp/rank1-7b) | Qwen2.5-7B | Smaller variant (7B parameters) |
-| [rank1-14b](https://huggingface.co/jhu-clsp/rank1-14b) | Qwen2.5-14B | Larger variant (14B parameters) |
-| [rank1-32b](https://huggingface.co/jhu-clsp/rank1-32b) | Qwen2.5-32B | Current model (32B parameters) |
-| [rank1-mistral-2501-24b](https://huggingface.co/jhu-clsp/rank1-mistral-2501-24b) | Mistral-Small 2501 24B | Trained from Mistral base |
-| [rank1-llama3-8b](https://huggingface.co/jhu-clsp/rank1-llama3-8b) | Llama 3.1 8B | Trained from Llama 3.1 base |
-
-### Quantized Variants
-
-| Model | Description |
-|:------|:------------|
-| [rank1-7b-awq](https://huggingface.co/jhu-clsp/rank1-7b-awq) | Quantized version of rank1-7b |
-| [rank1-14b-awq](https://huggingface.co/jhu-clsp/rank1-14b-awq) | Quantized version of rank1-14b |
-| [rank1-32b-awq](https://huggingface.co/jhu-clsp/rank1-32b-awq) | Quantized version of rank1-32b |
-| [rank1-mistral-2501-24b-awq](https://huggingface.co/jhu-clsp/rank1-mistral-2501-24b-awq) | Quantized version of rank1-mistral-24b |
-| [rank1-llama3-8b-awq](https://huggingface.co/jhu-clsp/rank1-llama3-8b-awq) | Quantized version of rank1-llama3-8b |
-
-## Associated Data and Resources
-
-| Resource | Description |
-|:---------|:------------|
-| [rank1-r1-msmarco](https://huggingface.co/datasets/jhu-clsp/rank1-r1-msmarco) | All R1 output examples from MS MARCO |
-| [rank1-training-data](https://huggingface.co/datasets/jhu-clsp/rank1-training-data) | Training data used for rank1 models |
-| [rank1-run-files](https://huggingface.co/datasets/jhu-clsp/rank1-run-files) | Pre-computed run files for use in top 100 doc reranking |
-| [GitHub Repository](https://github.com/orionw/rank1) | Official rank1 repository |
-
-## Usage
-Note that official usage is found on the Github and accounts for edge cases. But for simple use cases the minimal example below works.
-
-<details>
-<summary>Click to expand: Minimal example with vLLM</summary>
-
-```python
-from vllm import LLM, SamplingParams
-import math
-
-# Initialize the model with vLLM
-model = LLM(
-    model="jhu-clsp/rank1-32b",
-    tensor_parallel_size=1,  # Number of GPUs
-    trust_remote_code=True,
-    max_model_len=16000,     # Context length
-    gpu_memory_utilization=0.9,
-    dtype="float16",
-)
-
-# Set up sampling parameters
-sampling_params = SamplingParams(
-    temperature=0,
-    max_tokens=8192,
-    logprobs=20,
-    stop=["</think> true", "</think> false"],
-    skip_special_tokens=False
-)
-
-# Prepare the prompt
-def create_prompt(query, document):
-    return (
-        "Determine if the following passage is relevant to the query. "
-        "Answer only with 'true' or 'false'.\n"
-        f"Query: {query}\n"
-        f"Passage: {document}\n"
-        "<think>"
-    )
-
-# Example usage
-query = "What are the effects of climate change?"
-document = "Climate change leads to rising sea levels, extreme weather events, and disruptions to ecosystems. These effects are caused by increasing greenhouse gas concentrations in the atmosphere due to human activities."
-
-# Generate prediction
-prompt = create_prompt(query, document)
-outputs = model.generate([prompt], sampling_params)
-
-# Extract score
-output = outputs[0].outputs[0]
-text = output.text
-final_logits = output.logprobs[-1]
-
-# Get token IDs for "true" and "false" tokens
-from transformers import AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/rank1-32b")
-true_token = tokenizer(" true", add_special_tokens=False).input_ids[0]
-false_token = tokenizer(" false", add_special_tokens=False).input_ids[0]
-
-# Calculate relevance score (probability of "true")
-true_logit = final_logits[true_token].logprob
-false_logit = final_logits[false_token].logprob
-true_score = math.exp(true_logit)
-false_score = math.exp(false_logit)
-relevance_score = true_score / (true_score + false_score)
-
-print(f"Reasoning chain: {text}")
-print(f"Relevance score: {relevance_score}")
-```
-
-</details>
-
-## Performance
-
-rank1-32b demonstrates strong performance on retrieval benchmarks, particularly on tasks requiring complex reasoning. The model's ability to "think through" relevance decisions makes it especially effective for nuanced topics.
-
-For specific benchmark results and comparisons with other models, please refer to the paper and the official GitHub repository.
-
-## Installation
-
-Please see the Github for detailed installation instructions.
-
-## MTEB Integration
-
-rank1 is compatible with the [MTEB benchmarking framework](https://github.com/embeddings-benchmark/mteb):\
-
-```python
-from mteb import MTEB
-from rank1 import rank1  # From the official repo
-
-# Initialize the model
-model = rank1(
-    model_name_or_path="jhu-clsp/rank1-7b",
-    num_gpus=1,
-    device="cuda"
-)
-
-# Run evaluation on specific tasks
-evaluation = MTEB(tasks=["NevIR"])
-results = evaluation.run(model)
-```
-
-## Citation
-
-If you use rank1 in your research, please cite our work:
-
-```bibtex
-@misc{weller2025rank1testtimecomputereranking,
-      title={Rank1: Test-Time Compute for Reranking in Information Retrieval}, 
-      author={Orion Weller and Kathryn Ricci and Eugene Yang and Andrew Yates and Dawn Lawrie and Benjamin Van Durme},
-      year={2025},
-      eprint={2502.18418},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR},
-      url={https://arxiv.org/abs/2502.18418}, 
-}
-```
-
-## License
-
+---
+base_model:
+- Qwen/Qwen2.5-32B
+datasets:
+- jhu-clsp/rank1-training-data
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+license: mit
+pipeline_tag: text-ranking
+library_name: transformers
+tags:
+- reranker
+- retrieval
+---
+
+# rank1-32b: Test-Time Compute for Reranking in Information Retrieval
+
+📄 [Paper](https://arxiv.org/abs/2502.18418) | 🚀 [GitHub Repository](https://github.com/orionw/rank1)
+
+rank1 is a reasoning reranker model that "thinks" before making relevance judgments. This 32B parameter model is trained from the Qwen2.5-32B base model and leverages test-time compute to generate reasoning chains before deciding if a document is relevant to a query.
+
+## Model Description
+
+rank1 introduces a novel approach to information retrieval by generating explicit reasoning chains before making relevance judgments. Unlike traditional rerankers that directly output scores, rank1:
+
+1. Receives a query and document pair
+2. Generates a reasoning chain within a `<think>...</think>` section
+3. Makes a binary relevance judgment (`true` or `false`)
+4. Returns a confidence score based on the logits of the true/false tokens
+
+This approach helps the model break down complex relevance decisions into logical steps, improving performance across diverse retrieval tasks.
+
+## Model Family
+
+| Model | Base | Description |
+|:------|:-----|:------------|
+| [rank1-0.5b](https://huggingface.co/jhu-clsp/rank1-0.5b) | Qwen2.5-0.5B | Smallest variant (0.5B parameters) |
+| [rank1-1.5b](https://huggingface.co/jhu-clsp/rank1-1.5b) | Qwen2.5-1.5B | Smaller variant (1.5B parameters) |
+| [rank1-3b](https://huggingface.co/jhu-clsp/rank1-3b) | Qwen2.5-3B | Smaller variant (3B parameters) |
+| [rank1-7b](https://huggingface.co/jhu-clsp/rank1-7b) | Qwen2.5-7B | Smaller variant (7B parameters) |
+| [rank1-14b](https://huggingface.co/jhu-clsp/rank1-14b) | Qwen2.5-14B | Larger variant (14B parameters) |
+| [rank1-32b](https://huggingface.co/jhu-clsp/rank1-32b) | Qwen2.5-32B | Current model (32B parameters) |
+| [rank1-mistral-2501-24b](https://huggingface.co/jhu-clsp/rank1-mistral-2501-24b) | Mistral-Small 2501 24B | Trained from Mistral base |
+| [rank1-llama3-8b](https://huggingface.co/jhu-clsp/rank1-llama3-8b) | Llama 3.1 8B | Trained from Llama 3.1 base |
+
+### Quantized Variants
+
+| Model | Description |
+|:------|:------------|
+| [rank1-7b-awq](https://huggingface.co/jhu-clsp/rank1-7b-awq) | Quantized version of rank1-7b |
+| [rank1-14b-awq](https://huggingface.co/jhu-clsp/rank1-14b-awq) | Quantized version of rank1-14b |
+| [rank1-32b-awq](https://huggingface.co/jhu-clsp/rank1-32b-awq) | Quantized version of rank1-32b |
+| [rank1-mistral-2501-24b-awq](https://huggingface.co/jhu-clsp/rank1-mistral-2501-24b-awq) | Quantized version of rank1-mistral-24b |
+| [rank1-llama3-8b-awq](https://huggingface.co/jhu-clsp/rank1-llama3-8b-awq) | Quantized version of rank1-llama3-8b |
+
+## Associated Data and Resources
+
+| Resource | Description |
+|:---------|:------------|
+| [rank1-r1-msmarco](https://huggingface.co/datasets/jhu-clsp/rank1-r1-msmarco) | All R1 output examples from MS MARCO |
+| [rank1-training-data](https://huggingface.co/datasets/jhu-clsp/rank1-training-data) | Training data used for rank1 models |
+| [rank1-run-files](https://huggingface.co/datasets/jhu-clsp/rank1-run-files) | Pre-computed run files for use in top 100 doc reranking |
+| [GitHub Repository](https://github.com/orionw/rank1) | Official rank1 repository |
+
+## Usage
+Note that official usage is found on the Github and accounts for edge cases. But for simple use cases the minimal example below works.
+
+<details>
+<summary>Click to expand: Minimal example with vLLM</summary>
+
+```python
+from vllm import LLM, SamplingParams
+import math
+
+# Initialize the model with vLLM
+model = LLM(
+    model="jhu-clsp/rank1-32b",
+    tensor_parallel_size=1,  # Number of GPUs
+    trust_remote_code=True,
+    max_model_len=16000,     # Context length
+    gpu_memory_utilization=0.9,
+    dtype="float16",
+)
+
+# Set up sampling parameters
+sampling_params = SamplingParams(
+    temperature=0,
+    max_tokens=8192,
+    logprobs=20,
+    stop=["</think> true", "</think> false"],
+    skip_special_tokens=False
+)
+
+# Prepare the prompt
+def create_prompt(query, document):
+    return (
+        "Determine if the following passage is relevant to the query. "
+        "Answer only with 'true' or 'false'.\n"
+        f"Query: {query}\n"
+        f"Passage: {document}\n"
+        "<think>"
+    )
+
+# Example usage
+query = "What are the effects of climate change?"
+document = "Climate change leads to rising sea levels, extreme weather events, and disruptions to ecosystems. These effects are caused by increasing greenhouse gas concentrations in the atmosphere due to human activities."
+
+# Generate prediction
+prompt = create_prompt(query, document)
+outputs = model.generate([prompt], sampling_params)
+
+# Extract score
+output = outputs[0].outputs[0]
+text = output.text
+final_logits = output.logprobs[-1]
+
+# Get token IDs for "true" and "false" tokens
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/rank1-32b")
+true_token = tokenizer(" true", add_special_tokens=False).input_ids[0]
+false_token = tokenizer(" false", add_special_tokens=False).input_ids[0]
+
+# Calculate relevance score (probability of "true")
+true_logit = final_logits[true_token].logprob
+false_logit = final_logits[false_token].logprob
+true_score = math.exp(true_logit)
+false_score = math.exp(false_logit)
+relevance_score = true_score / (true_score + false_score)
+
+print(f"Reasoning chain: {text}")
+print(f"Relevance score: {relevance_score}")
+```
+
+</details>
+
+## Performance
+
+rank1-32b demonstrates strong performance on retrieval benchmarks, particularly on tasks requiring complex reasoning. The model's ability to "think through" relevance decisions makes it especially effective for nuanced topics.
+
+For specific benchmark results and comparisons with other models, please refer to the paper and the official GitHub repository.
+
+## Installation
+
+Please see the Github for detailed installation instructions.
+
+## MTEB Integration
+
+rank1 is compatible with the [MTEB benchmarking framework](https://github.com/embeddings-benchmark/mteb):\
+
+```python
+from mteb import MTEB
+from rank1 import rank1  # From the official repo
+
+# Initialize the model
+model = rank1(
+    model_name_or_path="jhu-clsp/rank1-7b",
+    num_gpus=1,
+    device="cuda"
+)
+
+# Run evaluation on specific tasks
+evaluation = MTEB(tasks=["NevIR"])
+results = evaluation.run(model)
+```
+
+## Citation
+
+If you use rank1 in your research, please cite our work:
+
+```bibtex
+@misc{weller2025rank1testtimecomputereranking,
+      title={Rank1: Test-Time Compute for Reranking in Information Retrieval}, 
+      author={Orion Weller and Kathryn Ricci and Eugene Yang and Andrew Yates and Dawn Lawrie and Benjamin Van Durme},
+      year={2025},
+      eprint={2502.18418},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2502.18418}, 
+}
+```
+
+## License
+
 [MIT License](https://github.com/orionw/rank1/blob/main/LICENSE)