Card: drop em dashes

README.md

@@ -15,7 +15,7 @@ tags:
 
 <img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft/resolve/main/banner.png" alt="SmolDataEnvs" width="100%">
 
-# 🛠️ SmolDataEnvs — SFT
+# 🛠️ SmolDataEnvs: SFT
 
 [![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)
 
@@ -28,7 +28,7 @@ tags:
 <img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft/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>
 
@@ -44,14 +44,14 @@ Drop-in ready for [TRL](https://github.com/huggingface/trl): conversational `mes
 ## What's inside
 
 - **4,677 correct trajectories**, one per task
-- **Difficulty** — easy 1,402 · medium 2,640 · hard 635
+- **Difficulty**: easy 1,402 · medium 2,640 · hard 635
 - One tool throughout: `bash`
 
 ## What's in a row
 
-- **`messages`** — the full conversation in OpenAI/TRL chat format: `system` → `user` (the task) →
+- **`messages`**: the full conversation in OpenAI/TRL chat format: `system` → `user` (the task) →
   `assistant` (reasoning plus `tool_calls`) → `tool` (command output) → … → final `assistant` answer
-- **`tools`** — the `bash` tool's JSON schema, for `apply_chat_template(..., tools=...)`
+- **`tools`**: the `bash` tool's JSON schema, for `apply_chat_template(..., tools=...)`
 - `task_id`, `difficulty` (1–5), `difficulty_tier`, `n_turns`, `source_agent`
 
 ## Fine-tune with TRL
@@ -74,8 +74,8 @@ A runnable notebook and a single-file script for HF Jobs are in
 
 ## 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.
@@ -89,7 +89,7 @@ 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 |