paraphrase-multilingual-MiniLM-L12-v2 trained on all_nli [32000]
This is a Cross Encoder model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the all-nli dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
Model Details
Model Description
- Model Type: Cross Encoder
- Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- Maximum Sequence Length: 512 tokens
- Number of Output Labels: 1 label
- Training Dataset:
- Language: en
- License: apache-2.0
Model Sources
- Documentation: Sentence Transformers Documentation
- Documentation: Cross Encoder Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Cross Encoders on Hugging Face
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("Borsa356/NLI_MNRL_EN_paraphrase-multilingual-MiniLM-L12-v2")
# Get scores for pairs of texts
pairs = [
['Two women are embracing while holding to go packages.', 'Two woman are holding packages.'],
['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids in numbered jerseys wash their hands.'],
['A man selling donuts to a customer during a world exhibition event held in the city of Angeles', 'A man selling donuts to a customer.'],
['Two young boys of opposing teams play football, while wearing full protection uniforms and helmets.', 'boys play football'],
['A man in a blue shirt standing in front of a garage-like structure painted with geometric designs.', 'A man is wearing a blue shirt'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'Two women are embracing while holding to go packages.',
[
'Two woman are holding packages.',
'Two kids in numbered jerseys wash their hands.',
'A man selling donuts to a customer.',
'boys play football',
'A man is wearing a blue shirt',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
Training Details
Training Dataset
all-nli
- Dataset: all-nli at d482672
- Size: 16,000 training samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 18 characters
- mean: 34.88 characters
- max: 193 characters
- min: 15 characters
- mean: 46.49 characters
- max: 181 characters
- min: 16 characters
- mean: 50.47 characters
- max: 204 characters
- Samples:
anchor positive negative A person on a horse jumps over a broken down airplane.
A person is outdoors, on a horse.
A person is at a diner, ordering an omelette.
Children smiling and waving at camera
There are children present
The kids are frowning
A boy is jumping on skateboard in the middle of a red bridge.
The boy does a skateboarding trick.
The boy skates down the sidewalk.
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 10.0, "num_negatives": 4, "activation_fn": "torch.nn.modules.activation.Sigmoid" }
Evaluation Dataset
all-nli
- Dataset: all-nli at d482672
- Size: 658 evaluation samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 658 samples:
anchor positive negative type string string string details - min: 15 characters
- mean: 72.88 characters
- max: 212 characters
- min: 12 characters
- mean: 34.13 characters
- max: 126 characters
- min: 11 characters
- mean: 36.55 characters
- max: 121 characters
- Samples:
anchor positive negative Two women are embracing while holding to go packages.
Two woman are holding packages.
The men are fighting outside a deli.
Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.
Two kids in numbered jerseys wash their hands.
Two kids in jackets walk to school.
A man selling donuts to a customer during a world exhibition event held in the city of Angeles
A man selling donuts to a customer.
A woman drinks her coffee in a small cafe.
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 10.0, "num_negatives": 4, "activation_fn": "torch.nn.modules.activation.Sigmoid" }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 16per_device_eval_batch_size
: 16learning_rate
: 2e-05num_train_epochs
: 1warmup_ratio
: 0.1
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsehub_revision
: Nonegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseliger_kernel_config
: Noneeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportionalrouter_mapping
: {}learning_rate_mapping
: {}
Training Logs
Epoch | Step | Training Loss |
---|---|---|
0.5 | 500 | 0.4526 |
1.0 | 1000 | 0.3647 |
Framework Versions
- Python: 3.13.5
- Sentence Transformers: 5.0.0
- Transformers: 4.53.0
- PyTorch: 2.7.1+cpu
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2
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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