kelompoknlp2026dsindo/deberta-v3-xsmall-nli-finetuned

CrossEncoder based on cross-encoder/nli-deberta-v3-xsmall

This is a Cross Encoder model finetuned from cross-encoder/nli-deberta-v3-xsmall using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text pair classification.

Model Details

Model Description

• —Model Type: Cross Encoder
• —Base model: cross-encoder/nli-deberta-v3-xsmall <!-- at revision a150876415327c80daeff35ca6f68f5ed8cf5c24 -->
• —Maximum Sequence Length: 512 tokens
• —Number of Output Labels: 3 labels
• —Supported Modality: Text <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Documentation: Cross Encoder Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Cross Encoders on Hugging Face

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'DebertaV2ForSequenceClassification'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("kelompoknlp2026dsindo/deberta-v3-xsmall-nli-finetuned")
# Get scores for pairs of inputs
pairs = [
    ['[South Australia] has the most expensive electricity in the world.', '[citation needed] South Australia has the highest retail price for electricity in the country.'],
    ['[South Australia] has the most expensive electricity in the world.', '"South Australia has the highest power prices in the world".'],
    ['when 3 per cent of total annual global emissions of carbon dioxide are from humans and Australia prod\xaduces 1.3 per cent of this 3 per cent, then no amount of emissions reductio\xadn here will have any effect on global climate.', 'The 2011 UNEP Green Economy report states that "[a]agricultural operations, excluding land use changes, produce approximately 13 per cent of anthropogenic global GHG emissions.'],
    ['when 3 per cent of total annual global emissions of carbon dioxide are from humans and Australia prod\xaduces 1.3 per cent of this 3 per cent, then no amount of emissions reductio\xadn here will have any effect on global climate.', 'With a market share of 30% and (potentially) clean electricity, heat pumps could reduce global CO 2 emissions by 8% annually.'],
    ['when 3 per cent of total annual global emissions of carbon dioxide are from humans and Australia prod\xaduces 1.3 per cent of this 3 per cent, then no amount of emissions reductio\xadn here will have any effect on global climate.', 'In the modern era, emissions to the atmosphere from volcanoes are approximately 0.645 billion tonnes of CO 2 per year, whereas humans contribute 29 billion tonnes of CO 2 each year.'],
]
scores = model.predict(pairs)
print(scores)
# [[-5.1375  3.7547 -0.2361]
#  [-4.5641  3.9137 -1.1062]
#  [ 1.3195 -2.9018  1.4899]
#  [ 0.1377 -2.4617  2.3478]
#  [ 1.4282 -2.7619  1.3155]]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Cross Encoder Classification

• —Datasets: DeBERTa-xsmall-finetune-dev, Claim-Evidence-train and Claim-Evidence-3Class-dev
• —Evaluated with <code>CrossEncoderClassificationEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 3,730 training samples
• —Columns: <code>claim</code>, <code>evidence</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | claim | evidence | label | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 8 tokens</li><li>mean: 25.8 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 37.91 tokens</li><li>max: 459 tokens</li></ul> | <ul><li>0: ~11.10%</li><li>1: ~35.90%</li><li>2: ~53.00%</li></ul> |
• —Samples: | claim | evidence | label | |:----------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>El Niño drove record highs in global temperatures suggesting rise may not be down to man-made emissions.</code> | <code>While ‘climate change’ can be due to natural forces or human activity, there is now substantial evidence to indicate that human activity – and specifically increased greenhouse gas (GHGs) emissions – is a key factor in the pace and extent of global temperature increases.</code> | <code>0</code> | | <code>El Niño drove record highs in global temperatures suggesting rise may not be down to man-made emissions.</code> | <code>This acceleration is due mostly to human-caused global warming, which is driving thermal expansion of seawater and the melting of land-based ice sheets and glaciers.</code> | <code>0</code> | | <code>In 1946, PDO switched to a cool phase.</code> | <code>There is evidence of reversals in the prevailing polarity (meaning changes in cool surface waters versus warm surface waters within the region) of the oscillation occurring around 1925, 1947, and 1977; the last two reversals corresponded with dramatic shifts in salmon production regimes in the North Pacific Ocean.</code> | <code>1</code> |
• —Loss: <code>CrossEntropyLoss</code>

Evaluation Dataset

Unnamed Dataset

• —Size: 433 evaluation samples
• —Columns: <code>claim</code>, <code>evidence</code>, and <code>label</code>
• —Approximate statistics based on the first 433 samples: | | claim | evidence | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 7 tokens</li><li>mean: 27.42 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 35.4 tokens</li><li>max: 217 tokens</li></ul> | <ul><li>0: ~13.16%</li><li>1: ~39.49%</li><li>2: ~47.34%</li></ul> |
• —Samples: | claim | evidence | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>[South Australia] has the most expensive electricity in the world.</code> | <code>[citation needed] South Australia has the highest retail price for electricity in the country.</code> | <code>1</code> | | <code>[South Australia] has the most expensive electricity in the world.</code> | <code>"South Australia has the highest power prices in the world".</code> | <code>1</code> | | <code>when 3 per cent of total annual global emissions of carbon dioxide are from humans and Australia prod­uces 1.3 per cent of this 3 per cent, then no amount of emissions reductio­n here will have any effect on global climate.</code> | <code>The 2011 UNEP Green Economy report states that "[a]agricultural operations, excluding land use changes, produce approximately 13 per cent of anthropogenic global GHG emissions.</code> | <code>2</code> |
• —Loss: <code>CrossEntropyLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 16
• —gradient_accumulation_steps: 2
• —learning_rate: 3e-05
• —weight_decay: 0.05
• —num_train_epochs: 5
• —lr_scheduler_type: cosine
• —warmup_steps: 0.1
• —load_best_model_at_end: True

All Hyperparameters

<details><summary>Click to expand</summary>

• —do_predict: False
• —prediction_loss_only: True
• —per_device_train_batch_size: 16
• —per_device_eval_batch_size: 8
• —gradient_accumulation_steps: 2
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 3e-05
• —weight_decay: 0.05
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1.0
• —num_train_epochs: 5
• —max_steps: -1
• —lr_scheduler_type: cosine
• —lr_scheduler_kwargs: None
• —warmup_ratio: None
• —warmup_steps: 0.1
• —log_level: passive
• —log_level_replica: warning
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —enable_jit_checkpoint: False
• —save_on_each_node: False
• —save_only_model: False
• —restore_callback_states_from_checkpoint: False
• —use_cpu: False
• —seed: 42
• —data_seed: None
• —bf16: False
• —fp16: False
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —local_rank: -1
• —ddp_backend: None
• —debug: []
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_prefetch_factor: None
• —disable_tqdm: False
• —remove_unused_columns: True
• —label_names: None
• —load_best_model_at_end: True
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —optim_args: None
• —group_by_length: False
• —length_column_name: length
• —project: huggingface
• —trackio_space_id: trackio
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —skip_memory_metrics: True
• —push_to_hub: False
• —resume_from_checkpoint: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_private_repo: None
• —hub_always_push: False
• —hub_revision: None
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —include_for_metrics: []
• —eval_do_concat_batches: True
• —auto_find_batch_size: False
• —full_determinism: False
• —ddp_timeout: 1800
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —include_num_input_tokens_seen: no
• —neftune_noise_alpha: None
• —optim_target_modules: None
• —batch_eval_metrics: False
• —eval_on_start: False
• —use_liger_kernel: False
• —liger_kernel_config: None
• —eval_use_gather_object: False
• —average_tokens_across_devices: True
• —use_cache: False
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Training Time

• —Training: 14.3 minutes

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 5.4.1
• —Transformers: 5.0.0
• —PyTorch: 2.10.0+cu128
• —Accelerate: 1.13.0
• —Datasets: 4.0.0
• —Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

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