FineCat-NLI v2 Large

Table of Contents

• Overview
• NLI Evaluation Results
• Usage
• Recommended Applications
• Extended Evaluation (45 datasets)
• Acknowledgments
• Citation

Overview

This model is a fine-tune of jhu-clsp/ettin-encoder-400m, trained on the dleemiller/FineCat-NLI-Reason dataset, a compilation of several high-quality NLI data sources with quality screening and reduction of easy samples in the training split. Also includes additional NLI, fact verification and long document sources (2.05M pairs in total). The training uses logit distillation from an NLI finetune of Qwen3.8-27B.

Distillation loss looks like this: $$ \begin{equation} \mathcal{L} = \alpha \cdot \mathcal{L}_{\text{CE}}(z^{(s)}, y) + \beta \cdot T^2 \cdot \mathrm{KL}\left(\sigma(z^{(t)}/T) ,|, \sigma(z^{(s)}/T)\right) \end{equation} $$

where \(z^{(s)}\) and \(z^{(t)}\) are the student and teacher logits, \(y\) are the ground truth labels, \(\alpha = 0.2\), \(\beta = 0.8\) and \(T = 1\).

Training inputs keep the full documents the teacher labelled (up to 8k tokens). The model was trained for 3 epochs at a constant learning rate followed by a 1-epoch cooldown. The released weights are an average of the final weights and earlier exponential-moving-average checkpoints, with the output temperature calibrated on the validation split.

NLI Evaluation Results

F1-Micro scores (equivalent to accuracy) for each dataset. Performance was measured at bs=32 using a Nvidia Blackwell PRO 6000 Max-Q.

Usage

Label Map:

• entailment: 0
• neutral: 1
• contradiction: 2

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load the model and run inference.

from sentence_transformers import CrossEncoder
import numpy as np

model = CrossEncoder("dleemiller/finecat-nli-v2-l")
id2label = model.model.config.id2label  # {0:'entailment', 1:'neutral', 2:'contradiction'}

pairs = [
    ("The glass fell off the counter and shattered on the tile.",
     "The glass broke when it hit the floor."),          # E
    ("The store opens at 9 a.m. every day.",
     "The store opens at 7 a.m. on weekdays."),          # C
    ("A researcher presented results at the conference.",
     "The presentation won the best paper award."),       # N
    ("It started raining heavily, so the match was postponed.",
     "The game was delayed due to weather."),             # E
    ("Every seat on the flight was taken.",
     "There were several empty seats on the plane."),     # C
]

logits = model.predict(pairs)  # shape: (5, 3)

for (prem, hyp), row in zip(pairs, logits):
    pred_idx = int(np.argmax(row))
    pred = id2label[pred_idx]
    print(f"[{pred}]  Premise: {prem}  |  Hypothesis: {hyp}")

Recommended Applications

Frame each check as a premise (text) and hypothesis (statement), and use the entailment probability.

Less suited: news-summary factuality (frank), financial and general legal verification (findver, legal_domain), and adversarial reasoning (anli_r3, nope_adv).

Extended Evaluation (45 datasets)

Accuracy on the test splits of 45 NLI and adjacent datasets. 2-way datasets (entailed vs. not) are scored as entailment vs. any non-entailment prediction. DocNLI and VitaminC are evaluated on fixed random subsets of 1,000 and 10,000 pairs.

Macro average over the 45 datasets: 0.8162.

Acknowledgments

We thank the creators and contributors of jhu-clsp (Ettin) and the Qwen team for making their work available. This model would not be possible without their efforts and open source contributions.

Citation

@misc{nli-compiled-2025,
  title = {FineCat NLI Dataset},
  author = {Lee Miller},
  year = {2025},
  howpublished = {Refined compilation of 6 major NLI datasets}
}