Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
License:
File size: 5,067 Bytes
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---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- agent
- coding
- terminal
- shell
- pretrain
- pretraining
- agentic
- llm
- web
- fineweb
- dclm
pretty_name: WebTerminal
size_categories:
- 1M<n<10M
configs:
- config_name: clean
data_files:
- split: train
path: clean/*.parquet
default: true
- config_name: unfiltered
data_files:
- split: train
path: unfiltered/*.parquet
---
# Terminal/CLI Web Text
A filtered extract of terminal and command-line content from two large web-text corpora, designed for upsampling agentic-adjacent data during pretraining.
## Subsets
| Subset | Rows | Tokens | Size | Quality |
|---|---|---|---|---|
| **`clean`** (default) | 2.33M | 4.6B | 11 GB | ~98% terminal content |
| `unfiltered` | 61.3M | 359B | 962 GB | ~15% terminal content |
```python
from datasets import load_dataset
# Load the clean subset (default)
ds = load_dataset("AdaMLLab/WebTerminal")
# Load the unfiltered subset
ds = load_dataset("AdaMLLab/WebTerminal", "unfiltered")
```
## Sources
- **DCLM** (`Zyphra/dclm-dedup`)
- **FineWeb** (`Salesforce/fineweb_deduplicated`)
## How it was built
### v0.1 Unfiltered
1. **Fast filter**: skip any document that doesn't contain obvious CLI indicators (`$`, `sudo`, `pip install`, `` ```bash ``, `root@`, etc.)
2. **Score**: remaining docs are scored (0-34) across five signals, each with a per-match point value and a cap:
| Filter | Description | Points | Cap |
|---|---|---|---|
| Prompt patterns | Shell prompts like `$ cmd`, `user@host:~$`, `>>>`, `root@`, `PS C:\` | 2 per match | 10 |
| CLI commands | Known commands: `sudo`, `apt-get`, `pip install`, `git clone`, `docker run`, `curl`, `ssh`, `gcc`, etc. (30+ patterns) | 1 per unique match | 8 |
| stdout patterns | Output indicators: "successfully installed", "cloning into", `drwx` (ls output), "packets transmitted", "traceback", version strings | 2 per match | 6 |
| Code blocks | Terminal-flavored code blocks: `` ```bash ``, `` ```shell ``, `<pre><code>`, terminal/console div classes | 2 per match | 6 |
| Indented blocks | 3+ consecutive lines indented 4+ spaces (code/output blocks) | 1 per match | 4 |
Documents scoring >=5 are kept.
3. **Dedup**: exact dedup across both datasets using xxhash64 on full text. Removed 1,168 duplicates.
### v0.2 Clean
The unfiltered subset is ~84-86% noise at lower score levels (5-12), which make up 93% of the data. The root cause: v0.1's scoring uses context-blind keyword matching, CLI command names like `find`, `make`, `cat` appear in normal English prose, bare `$` matches currency amounts, and indented Python/SQL code gets scored as terminal content.
v0.2 applies a three-stage structural filter over the unfiltered data:
1. **Context-aware**: instead of matching bare `$`, requires `$ sudo`, `$ git`, `$ docker`, etc. (dollar sign + space + known command). Eliminates ~87% of documents immediately.
2. **Validation regex**: confirms a genuine structural terminal pattern exists, shell prompts followed by real commands, `user@host:~$` patterns, Python REPL `>>>`, tracebacks, `` ```bash `` code blocks, Unix file permission listings, man page headers, shebangs.
3. **Weighted structural scoring** (`term_score_v2`): each pattern has a weight (1-3) and occurrences are capped. Documents need `term_score_v2 >= 3` to be kept.
| Weight | Signal | Max |
|---|---|---|
| 3 | Command prompts (`$ cmd` at line start) | 9 |
| 3 | SSH prompts (`user@host:~$`) | 9 |
| 2 | Python REPL, file listings, tracebacks, terminal code blocks, git/docker ops, Windows prompts, man pages | 2-6 each |
| 1 | Install output, systemd units, shebangs, sudo commands | 1 each |
No indentation-based scoring. No context-blind command substring matching.
**Result**: 3.8% of the unfiltered data survives, from 61.3M rows down to 2.33M rows. Quality jumps from ~15% to ~98% terminal/CLI content.
## Schema
### Clean subset
| Column | Type | Description |
|---|---|---|
| `text` | string | Document text |
| `term_score` | int32 | Original v0.1 score (5-34) |
| `term_score_v2` | int32 | Structural score from v0.2 filter (3+) |
### Unfiltered subset
| Column | Type | Description |
|---|---|---|
| `text` | string | Document text |
| `term_score` | int32 | Original v0.1 score (5-34) |
## Stats
### Clean (v0.2)
- **2,334,414 rows** | **4.6B tokens** (Llama-3.2-1B tokenizer) | **11 GB**
- 62 parquet files, ~169-185 MB each, snappy compressed
### Unfiltered (v0.1)
- **61,341,278 rows** | **359B tokens** | **962 GB**
- 4,187 parquet files, ~180-240 MB each, snappy compressed
| v0.1 Score | Count | % |
|---|---|---|
| 5 | 39,025,201 | 63.62% |
| 6 | 10,787,199 | 17.59% |
| 7 | 4,063,886 | 6.63% |
| 8 | 2,911,983 | 4.75% |
| 9-14 | 3,594,547 | 5.86% |
| 15-34 | 958,462 | 1.56% |
## Use case
Upsampling agentic-adjacent data during pretraining. The `clean` subset is recommended for most use cases. The `unfiltered` subset is available for researchers who want to apply their own filtering.
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