bert-base-multilingual-cased-xnli-nli – AI Model by AyoubChLin | AlphaNeural AI

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AyoubChLin/
bert-base-multilingual-cased-xnli-nli

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natural-language-inference

text-classification

zero-shot-classification

google-bert/bert-base-multilingual-cased

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mBERT Fine-tuned on XNLI for Natural Language Inference

This model is a fine-tuned version of bert-base-multilingual-cased for 3-way Natural Language Inference using the facebook/xnli dataset.

Labels

ID	Label
0	entailment
1	neutral
2	contradiction

Training Configuration

Base model: bert-base-multilingual-cased
Architecture: BERT / mBERT sequence classification
Dataset: facebook/xnli
Dataset config: all_languages
Training language: en
Max sequence length: 512
Epochs: 3.0
Learning rate: 2e-05
Train batch size: 32
Eval batch size: 64
Weight decay: 0.01

Intended Use

This model predicts whether a hypothesis is entailed by, neutral with respect to, or contradicted by a premise.

Limitations

Performance should be validated on your target language and domain.
Inputs longer than 512 tokens are truncated.
The model predicts NLI labels and should not be used as a standalone factuality verifier.