File size: 4,585 Bytes
d9ab7d4 90db785 d9ab7d4 a4c1fac d9ab7d4 90db785 d9ab7d4 697121a 90db785 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: ground_truth
dtype: string
- name: dataset
dtype: string
- name: env_config
struct:
- name: env_name
dtype: string
- name: image
dtype: string
- name: task_id
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 21182796
num_examples: 4567
download_size: 20865488
dataset_size: 21182796
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
language:
- en
tags:
- tmax
- terminal
- agents
- reinforcement-learning
---
# SETA-Env for tmax
**Images require building:** the dataset conversion and complete task archive are published. 0 task image(s) were published during preparation; the remaining images are intentionally left for the included resumable build script. No build job is running for this release. Only tasks with available images can run immediately. See `image-build-status.json` for the publication-time snapshot.
All **4,567** task directories from [camel-ai/SETA-Env](https://huggingface.co/datasets/camel-ai/SETA-Env), pinned to `3c3bc8975b05826bf41769bd6b2da76ad1b6dd42`, converted to tmax's `swerl_vanillux_sandbox` format.
The train split uses the same `messages`, `ground_truth`, `dataset`, `env_config`, and `source` schema as the other `TMaxxx/agent-task-*` datasets. Messages use the tmax Vanillux templates; `dataset` is `passthrough`. Task IDs are prefixed with `seta_env__` to avoid collisions when combining task archives. `task-manifest.json` maps them back to original paths.
`task-data.tar.gz` contains a directory per task with all original task files preserved byte-for-byte, plus `image.txt`. Instructions, verifiers, build contexts, metadata, and reference solutions are retained. The tmax loader exposes only environment seed files and defers tests until submission; reference solutions are not mounted into the agent sandbox.
## Usage
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_download
train = load_dataset("TMaxxx/agent-task-seta-env-v1.1", split="train")
archive = hf_hub_download("TMaxxx/agent-task-seta-env-v1.1", "task-data.tar.gz", repo_type="dataset")
```
Extract the archive and set the sandbox `task_data_dir` to the directory containing the task directories. Every row specifies its image explicitly. Task resource requirements and verifier timeouts remain in the original `task.toml`; configure the training harness accordingly.
## Images
Images use `hamishi740/agent-task-seta-env:<source-environment-hash>` and target Linux AMD64. See `task-manifest.json` for build status.
## Validation and attribution
All rows passed schema and Parquet round-trip checks, and every archived task file was checked against the downloaded source bytes. See `validation.json`. This does not constitute an evaluation of every task or a full training run.
tmax Vanillux prompts are adapted from mini-swe-agent (MIT).
## Resume image builds
Requires Docker Buildx and a Docker Hub login with write access to the `hamishi740` target repositories. The script skips tags already published, retains per-image logs, and can be rerun after failures.
```sh
mkdir -p tasks
tar -xzf task-data.tar.gz -C tasks
python build_dataset_images.py --task-data-dir tasks --manifest task-manifest.json --workers 8 --timeout 1800
```
To use another Docker Hub namespace, update the image references consistently in the manifest, training records, and task `image.txt` files before building.
Runtime smoke tests were not run for SETA because image builds were deferred. Structural, schema, and archive checks passed for all tasks.
## Apptainer images
This release does not provide a hosted Apptainer image pool. Build the Docker images with the included `build_dataset_images.py` script above, then use `apptainer pull docker://<image-reference>` to create local SIFs. See `task-manifest.json` for the per-task image references and publication-time build status.
## Source and license
This is a tmax training-format conversion of the [original dataset](https://huggingface.co/datasets/camel-ai/SETA-Env). The original authors' license and reuse terms apply to the source data; consult the [upstream license and dataset documentation](https://huggingface.co/datasets/camel-ai/SETA-Env) for those terms, attribution, and citation requirements. This conversion does not relicense the source tasks or bundled third-party materials.
|