bigboub/clause-and-effect-models

Clause & Effect models

The two fine-tuned models behind the Clause & Effect Space. 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

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.