timchen0618/bcp-traj-ext-formatted-v1
收藏Hugging Face2026-04-08 更新2026-04-12 收录
下载链接：
https://hf-mirror.com/datasets/timchen0618/bcp-traj-ext-formatted-v1
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资源简介：
---
license: mit
tags:
- bcp
- traj-ext
- browsecomp-plus
- seed0
---
# bcp-traj-ext-formatted-v1
Trajectories from seed0 (gpt-oss-120b, Qwen3-Embedding-8B, full split) formatted in the traj_ext style: trajectory_text is the serialized steps ([Reasoning]/[Tool call]/[Tool result]/[Final answer]), and formatted_prompt is the full QUERY_TEMPLATE_GIVEN_TRAJECTORY prompt ready to feed to the next agent.
## Dataset Info
- **Rows**: 830
- **Columns**: 9
## Columns
| Column | Type | Description |
|--------|------|-------------|
| query_id | Value('string') | BrowseComp-Plus query ID |
| question | Value('string') | Raw question text |
| trajectory_text | Value('string') | Serialized trajectory: [Reasoning]/[Tool call]/[Tool result]/[Final answer] blocks joined by double newlines. Tool outputs truncated to 500 chars. |
| formatted_prompt | Value('string') | Full QUERY_TEMPLATE_GIVEN_TRAJECTORY prompt with question and <trajectory>...</trajectory> block — the exact string fed to the model in traj_ext mode. |
| status | Value('string') | Trajectory completion status ('completed' or 'incomplete') |
| n_steps | Value('int64') | Total number of result steps in the trajectory |
| n_tool_calls | Value('int64') | Number of tool call steps |
| n_reasoning_steps | Value('int64') | Number of reasoning steps |
| run_id | Value('string') | Source trajectory filename (without .json) |
## Generation Parameters
```json
{
"script_name": "format_traj_ext.py",
"model": "gpt-oss-120b",
"description": "Trajectories from seed0 (gpt-oss-120b, Qwen3-Embedding-8B, full split) formatted in the traj_ext style: trajectory_text is the serialized steps ([Reasoning]/[Tool call]/[Tool result]/[Final answer]), and formatted_prompt is the full QUERY_TEMPLATE_GIVEN_TRAJECTORY prompt ready to feed to the next agent.",
"cluster": "torch",
"artifact_status": "final",
"canary": false,
"hyperparameters": {},
"input_datasets": []
}
```
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("timchen0618/bcp-traj-ext-formatted-v1", split="train")
print(f"Loaded {len(dataset)} rows")
```
---
提供机构：
timchen0618
