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ModernBERT-large-nli

Zero-Shot Classification
Transformers
Safetensors
English
modernbert
text-classification
instruct
natural-language-inference
nli
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  library_name: transformers
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  ---
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  # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- ## How to Get Started with the Model
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- ## Training Details
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- #### Preprocessing [optional]
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  ---
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  library_name: transformers
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+ base_model:
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+ - answerdotai/ModernBERT-large
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+ license: apache-2.0
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+ language:
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+ - en
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+ pipeline_tag: zero-shot-classification
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+ datasets:
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+ - nyu-mll/glue
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+ - facebook/anli
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+ tags:
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+ - instruct
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+ - natural-language-inference
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+ - nli
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  ---
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  # Model Card for Model ID
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+ ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table).
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+ This is the equivalent of an "instruct" version.
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+ The model was trained for 200k steps on an Nvidia A30 GPU.
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+
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+ It is very good at reasoning tasks (better than llama 3.1 8B Instruct on ANLI and FOLIO), long context reasoning, sentiment analysis and zero-shot classification with new labels.
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+
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+ | test_name | test_accuracy |
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+ |:--------------------------------------|----------------:|
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+ | glue/mnli | 0.89 |
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+ | glue/qnli | 0.96 |
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+ | glue/rte | 0.91 |
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+ | glue/wnli | 0.64 |
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+ | glue/mrpc | 0.81 |
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+ | glue/qqp | 0.87 |
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+ | super_glue/boolq | 0.66 |
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+ | super_glue/cb | 0.86 |
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+ | super_glue/multirc | 0.9 |
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+ | super_glue/wic | 0.71 |
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+ | super_glue/axg | 1 |
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+ | anli/a1 | 0.72 |
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+ | anli/a2 | 0.54 |
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+ | anli/a3 | 0.55 |
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+ | sick/label | 0.91 |
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+ | sick/entailment_AB | 0.93 |
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+ | snli | 0.94 |
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+ | scitail/snli_format | 0.95 |
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+ | hans | 1 |
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+ | WANLI | 0.77 |
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+ | recast/recast_ner | 0.85 |
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+ | recast/recast_sentiment | 0.97 |
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+ | recast/recast_verbnet | 0.89 |
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+ | recast/recast_megaveridicality | 0.87 |
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+ | recast/recast_verbcorner | 0.87 |
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+ | recast/recast_kg_relations | 0.9 |
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+ | recast/recast_factuality | 0.95 |
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+ | recast/recast_puns | 0.98 |
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+ | probability_words_nli/reasoning_1hop | 1 |
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+ | probability_words_nli/usnli | 0.79 |
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+ | probability_words_nli/reasoning_2hop | 0.98 |
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+ | nan-nli | 0.85 |
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+ | nli_fever | 0.78 |
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+ | breaking_nli | 0.99 |
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+ | conj_nli | 0.72 |
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+ | fracas | 0.79 |
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+ | dialogue_nli | 0.94 |
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+ | mpe | 0.75 |
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+ | dnc | 0.91 |
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+ | recast_white/fnplus | 0.76 |
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+ | recast_white/sprl | 0.9 |
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+ | recast_white/dpr | 0.84 |
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+ | add_one_rte | 0.94 |
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+ | paws/labeled_final | 0.96 |
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+ | glue/cola | 0.87 |
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+ | glue/sst2 | 0.96 |
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+ | pragmeval/pdtb | 0.56 |
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+ | lex_glue/scotus | 0.58 |
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+ | lex_glue/ledgar | 0.85 |
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+ | dynasent/dynabench.dynasent.r1.all/r1 | 0.83 |
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+ | dynasent/dynabench.dynasent.r2.all/r2 | 0.76 |
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+ | cycic_classification | 0.96 |
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+ | lingnli | 0.91 |
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+ | monotonicity-entailment | 0.97 |
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+ | scinli | 0.88 |
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+ | naturallogic | 0.93 |
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+ | dynahate | 0.86 |
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+ | syntactic-augmentation-nli | 0.94 |
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+ | autotnli | 0.92 |
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+ | defeasible-nli/atomic | 0.83 |
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+ | defeasible-nli/snli | 0.8 |
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+ | help-nli | 0.96 |
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+ | nli-veridicality-transitivity | 0.99 |
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+ | lonli | 0.99 |
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+ | dadc-limit-nli | 0.79 |
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+ | folio | 0.71 |
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+ | tomi-nli | 0.54 |
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+ | puzzte | 0.59 |
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+ | temporal-nli | 0.93 |
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+ | counterfactually-augmented-snli | 0.81 |
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+ | cnli | 0.9 |
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+ | boolq-natural-perturbations | 0.72 |
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+ | equate | 0.65 |
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+ | logiqa-2.0-nli | 0.58 |
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+ | mindgames | 0.96 |
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+ | ConTRoL-nli | 0.66 |
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+ | logical-fallacy | 0.38 |
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+ | cladder | 0.89 |
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+ | conceptrules_v2 | 1 |
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+ | zero-shot-label-nli | 0.79 |
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+ | scone | 1 |
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+ | monli | 1 |
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+ | SpaceNLI | 1 |
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+ | propsegment/nli | 0.92 |
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+ | FLD.v2/default | 0.91 |
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+ | FLD.v2/star | 0.78 |
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+ | SDOH-NLI | 0.99 |
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+ | scifact_entailment | 0.87 |
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+ | feasibilityQA | 0.79 |
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+ | AdjectiveScaleProbe-nli | 1 |
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+ | resnli | 1 |
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+ | semantic_fragments_nli | 1 |
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+ | dataset_train_nli | 0.95 |
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+ | nlgraph | 0.97 |
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+ | ruletaker | 0.99 |
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+ | PARARULE-Plus | 1 |
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+ | logical-entailment | 0.93 |
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+ | nope | 0.56 |
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+ | LogicNLI | 0.91 |
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+ | contract-nli/contractnli_a/seg | 0.88 |
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+ | contract-nli/contractnli_b/full | 0.84 |
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+ | nli4ct_semeval2024 | 0.72 |
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+ | biosift-nli | 0.92 |
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+ | SIGA-nli | 0.57 |
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+ | FOL-nli | 0.79 |
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+ | doc-nli | 0.81 |
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+ | mctest-nli | 0.92 |
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+ | natural-language-satisfiability | 0.92 |
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+ | idioms-nli | 0.83 |
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+ | lifecycle-entailment | 0.79 |
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+ | MSciNLI | 0.84 |
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+ | hover-3way/nli | 0.92 |
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+ | seahorse_summarization_evaluation | 0.81 |
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+ | missing-item-prediction/contrastive | 0.88 |
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+ | Pol_NLI | 0.93 |
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+ | synthetic-retrieval-NLI/count | 0.72 |
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+ | synthetic-retrieval-NLI/position | 0.9 |
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+ | synthetic-retrieval-NLI/binary | 0.92 |
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+ | babi_nli | 0.98 |
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+
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+
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+
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+ # Usage
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+
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+ ## [ZS] Zero-shot classification pipeline
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+ ```python
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+ from transformers import pipeline
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+ classifier = pipeline("zero-shot-classification",model="tasksource/ModernBERT-large-nli")
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+
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+ text = "one day I will see the world"
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+ candidate_labels = ['travel', 'cooking', 'dancing']
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+ classifier(text, candidate_labels)
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+ ```
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+ NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
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+
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+ ## [NLI] Natural language inference pipeline
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+
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+ ```python
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+ from transformers import pipeline
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+ pipe = pipeline("text-classification",model="tasksource/ModernBERT-large-nli")
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+ pipe([dict(text='there is a cat',
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+ text_pair='there is a black cat')]) #list of (premise,hypothesis)
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+ ```
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+
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+ ## Backbone for further fune-tuning
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+
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+ This checkpoint has stronger reasoning and fine-grained abilities than the base version and can be used for further fine-tuning.
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+
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+ # Citation
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+
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+ ```
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+ @inproceedings{sileo-2024-tasksource,
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+ title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
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+ author = "Sileo, Damien",
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+ booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
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+ month = may,
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+ year = "2024",
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+ address = "Torino, Italia",
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+ publisher = "ELRA and ICCL",
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+ url = "https://aclanthology.org/2024.lrec-main.1361",
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+ pages = "15655--15684",
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+ }
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+ ```