Card: drop em dashes

README.md

@@ -17,7 +17,7 @@ tags:
 
 <img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train/resolve/main/banner.png" alt="SmolDataEnvs" width="100%">
 
-# 📊 SmolDataEnvs — Harbor (train)
+# 📊 SmolDataEnvs: Harbor (train)
 
 [![Collection](https://img.shields.io/badge/%F0%9F%A4%97%20Collection-SmolDataEnvs-FFD21E?style=for-the-badge&labelColor=1a1a1a)](https://huggingface.co/collections/FineEnvs/smoldataenvs)
 [![Harbor Visualiser](https://img.shields.io/badge/%F0%9F%A4%97%20Harbor%20Visualiser-Browse%20tasks-FFD21E?style=for-the-badge&labelColor=1a1a1a)](https://huggingface.co/spaces/HuggingFaceH4/harbor-visualiser?dataset=FineEnvs/SmolDataEnvs-harbor-train)
@@ -31,7 +31,7 @@ tags:
 <img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train/resolve/main/curves.gif" alt="Reward and held-out pass@k climbing over 1,119 GRPO steps" width="100%">
 
 <sub>A 2B model on these tasks. Left: what it optimises. Right: 144 held-out tasks it never trains on.<br>
-Two runs over the same 5,000 tasks — <b>shuffled</b> against a <b>curriculum</b> ordered easiest to hardest.</sub>
+Two runs over the same 5,000 tasks: <b>shuffled</b> against a <b>curriculum</b> ordered easiest to hardest.</sub>
 
 </div>
 
@@ -46,8 +46,8 @@ agentic environment: its own container, its own data, its own verifier.
 
 ## What's inside
 
-- **5,000 verified tasks** — the RL training set
-- **Difficulty** — easy **1,433** · medium **2,845** · hard **722** (`difficulty_tier`, plus `difficulty_level` 1–5)
+- **5,000 verified tasks**: the RL training set
+- **Difficulty**: easy **1,433** · medium **2,845** · hard **722** (`difficulty_tier`, plus `difficulty_level` 1–5)
 
 ## How a task is laid out
 
@@ -93,8 +93,8 @@ openenv harbor rollout \
 
 ## Where it comes from
 
-Built from the [jupyter-agent dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
-— real data-science notebooks over 471 Kaggle datasets. Every question–answer pair was extracted and
+Built from the [jupyter-agent dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset),
+real data-science notebooks over 471 Kaggle datasets. Every question–answer pair was extracted and
 then **verified**: strong agent models had to solve the task in a live sandbox and reproduce the gold
 answer under deterministic grading. Anything ambiguous or un-checkable was dropped. So every task
 here is known-solvable and unambiguously gradable.
@@ -108,7 +108,7 @@ when you change the grader's model, because there isn't one.
 
 | Repo | What it is |
 |---|---|
-| [`SmolDataEnvs`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs) | the tasks as plain rows — load it and prompt any model |
+| [`SmolDataEnvs`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs) | the tasks as plain rows, load it and prompt any model |
 | [`SmolDataEnvs-sft`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft) | 4,677 verified agent trajectories, TRL-ready |
 | [`SmolDataEnvs-harbor-train`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train) | 5,000 tasks as Harbor environments |
 | [`SmolDataEnvs-harbor-test`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-test) | 250 held-out, deliberately harder |