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---
license: mit
language:
- en
metrics:
- bertscore
- rouge
base_model:
- allenai/led-base-16384
---
# ToS Simplifier
`sa-ma/tos-simplifier` is a fine-tuned **Longformer Encoder–Decoder (LED)** model that turns dense, jargon-filled Terms of Service (ToS) documents into clear, plain-English summaries. The underlying LED architecture processes sequences up to 16 384 tokens in one pass, making it ideal for very long contracts.:contentReference[oaicite:0]{index=0}
## Model details
| | |
| --- | --- |
| **Base model** | `allenai/led-base-16384` |
| **Parameters** | ~162 M |
| **Context window** | 16 384 tokens (encoder) / 1 024 (decoder) |
| **Language** | English |
| **License** | MIT |
## Training
The model was fine-tuned on an internal corpus of publicly available ToS and their human-written “plain language” summaries (≈ 1.2 k document–summary pairs).
Key hyper-parameters:
* Optimiser — Adam W (β₁ = 0.9, β₂ = 0.98)
* Learning-rate — 3 × 10⁻⁵ with linear warm-up
* Batch — 16 effective (8 × 2 GPUs, gradient-accumulation = 2)
* Early-stop on validation ROUGE-L
Full settings are stored in `training_args.bin`.
## Intended use
| ✔ What it’s for | ✖ What it’s **not** for |
| --- | --- |
| Summarising ToS, privacy policies, EULAs | Non-English input |
| General long-form abstractive summarisation | Producing legally binding advice |
| Making legal texts more accessible | Summarising sensitive or proprietary data without review |
## Quick start
```python
from transformers import LEDTokenizer, LEDForConditionalGeneration, pipeline
model_id = "sa-ma/tos-simplifier"
summariser = pipeline(
"summarization",
model=model_id,
tokenizer=model_id,
device_map="auto", # drop or change if running on CPU
max_length=256,
min_length=30,
)
long_doc = open("tos.txt").read()
summary = summariser(long_doc)[0]["summary_text"]
print(summary)
|