HandongAILab
/

monli-bert-snli

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 ---
 library_name: transformers
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 ---
+language: en
+license: cc-by-sa-4.0
 library_name: transformers
+pipeline_tag: text-classification
+tags:
+- natural-language-inference
+- monotonicity
+- negation
+- reproduction
+- bert
+datasets:
+- stanfordnlp/snli
+base_model: bert-base-uncased
 ---
+# monli-bert-snli
+`bert-base-uncased` fine-tuned on **SNLI** for 3-way Natural Language Inference
+(entailment / neutral / contradiction).
+This is a **reproduction artifact** — the *Experiment 1.1 baseline* from our
+reproduction of:
+> Atticus Geiger, Kyle Richardson, Christopher Potts (2020).
+> *Neural Natural Language Inference Models Partially Embed Theories of Lexical
+> Entailment and Negation.* BlackboxNLP @ EMNLP 2020. [arXiv:2004.14623](https://arxiv.org/abs/2004.14623)
+It is **not** a novel model; it exists to evaluate how a standard SNLI-trained NLI
+model behaves on the [MoNLI](https://github.com/atticusg/MoNLI) monotonicity
+challenge set.
+## Training
+| Setting | Value |
+|---------|-------|
+| Base model | `bert-base-uncased` |
+| Data | SNLI train (~549K examples; rows with `label == -1` removed) |
+| Epochs | 3 |
+| Learning rate | 2e-5 |
+| Batch size | 48 |
+| Max length | 128 |
+| Warmup ratio | 0.1 |
+## Results
+Evaluated on SNLI test and on MoNLI (Geiger et al. paper figures in parentheses):
+| Dataset | Accuracy |
+|---------|----------|
+| SNLI test | 90.6% (paper 90.8%) |
+| PMoNLI (no negation) | 90.7% (paper 94.4%) |
+| NMoNLI (with negation) | 2.7% (paper 2.2%) |
+The core finding is reproduced: high accuracy without negation, **near-0% once
+negation reverses the entailment direction** — SNLI training provides essentially
+no signal for negation + monotonicity.
+## Labels
+| id | label |
+|----|-------|
+| 0 | entailment |
+| 1 | neutral |
+| 2 | contradiction |
+## Reproduction
+Companion model (after inoculation): [`HandongAILab/monli-bert-inoculated`](https://huggingface.co/HandongAILab/monli-bert-inoculated)
+## Limitations
+Trained only on SNLI; it does not handle negation-driven monotonicity and should
+not be used for production NLI. Inherits SNLI's known annotation artifacts.