| --- |
| language: en |
| tags: |
| - legal |
| - terms-of-service |
| - text-classification |
| - summarization |
| datasets: |
| - CodeHima/TOS_DatasetV3 |
| - EE21/ToS-Summaries |
| --- |
| |
| # Clause & Effect models |
|
|
| The two fine-tuned models behind the [Clause & Effect Space](https://huggingface.co/spaces/bigboub/clause-and-effect). |
| Weights are stored in fp16 to halve the download size; load them as fp32 (`dtype=torch.float32`) for CPU inference. |
|
|
| | Folder | Base model | Task | |
| |---|---|---| |
| | `bert/` | `nlpaueb/legal-bert-base-uncased` | 3-way clause fairness: `clearly_fair`, `potentially_unfair`, `clearly_unfair` | |
| | `bart/` | `facebook/bart-large-cnn` | Plain-English summary of a clause, input formatted as `"{label}: {clause}"` | |
|
|
| ## BERT test-set results (CodeHima/TOS_DatasetV3, 960 clauses) |
| |
| | Class | Precision | Recall | F1 | |
| |---|---|---|---| |
| | clearly_fair | 0.92 | 0.93 | 0.92 | |
| | potentially_unfair | 0.81 | 0.80 | 0.81 | |
| | clearly_unfair | 0.79 | 0.80 | 0.80 | |
| | **Accuracy** | | | **0.864** | |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, BartForConditionalGeneration |
| |
| repo = "bigboub/clause-and-effect-models" |
| bert_tok = AutoTokenizer.from_pretrained(repo, subfolder="bert") |
| bert = AutoModelForSequenceClassification.from_pretrained(repo, subfolder="bert", dtype=torch.float32) |
| bart_tok = AutoTokenizer.from_pretrained(repo, subfolder="bart") |
| bart = BartForConditionalGeneration.from_pretrained(repo, subfolder="bart", dtype=torch.float32) |
| ``` |
|
|
| ## Limitations |
|
|
| Trained on individual, clean ToS sentences. On full real-world documents the classifier |
| over-flags some boilerplate (e.g. "These Terms are an agreement between you and ...") as unfair. |
| Not legal advice. |
|
|
