SentenceTransformer based on FacebookAI/xlm-roberta-base
This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: FacebookAI/xlm-roberta-base
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
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 SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("slimaneMakh/triplet_CloseHlabel_farLabel_andnegativ-1M-5eps-XLMR_29may")
# Run inference
sentences = [
'Sales',
'Revenue',
'Operating profit',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Triplet
- Evaluated with
TripletEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.9988 |
dot_accuracy | 0.0015 |
manhattan_accuracy | 0.9975 |
euclidean_accuracy | 0.9991 |
max_accuracy | 0.9991 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 660,643 training samples
- Columns:
anchor_label
,pos_hlabel
, andneg_hlabel
- Approximate statistics based on the first 1000 samples:
anchor_label pos_hlabel neg_hlabel type string string string details - min: 3 tokens
- mean: 11.86 tokens
- max: 39 tokens
- min: 3 tokens
- mean: 9.06 tokens
- max: 32 tokens
- min: 3 tokens
- mean: 7.99 tokens
- max: 25 tokens
- Samples:
anchor_label pos_hlabel neg_hlabel Basic earnings (loss) per share
Tavakasum kahjum aktsia kohta
II Kapital z nadwyzki wartosci emisyjnej ponad wartosc nominalna
Comprehensive income
Suma dochodow calkowitych
dont Marques
Cash and cash equivalents
Cash and cash equivalents
Cars incl prepayments
- Loss:
TripletLoss
with these parameters:{ "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 }
Evaluation Dataset
Unnamed Dataset
- Size: 283,133 evaluation samples
- Columns:
anchor_label
,pos_hlabel
, andneg_hlabel
- Approximate statistics based on the first 1000 samples:
anchor_label pos_hlabel neg_hlabel type string string string details - min: 3 tokens
- mean: 11.78 tokens
- max: 37 tokens
- min: 3 tokens
- mean: 9.22 tokens
- max: 39 tokens
- min: 3 tokens
- mean: 8.12 tokens
- max: 29 tokens
- Samples:
anchor_label pos_hlabel neg_hlabel Deferred tax assets
Deferred tax assets
Immateriella tillgangar
Equity
EGET KAPITAL inklusive periodens resultat
Materials
Adjustments for decrease (increase) in other operating receivables
Okning av ovriga rorelsetillgangar
Rorelseresultat
- Loss:
TripletLoss
with these parameters:{ "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 1warmup_ratio
: 0.1batch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseprediction_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
: Nonelearning_rate
: 5e-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
: 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}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
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falsefp16_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
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | max_accuracy |
---|---|---|---|---|
0.0121 | 500 | 3.7705 | - | - |
0.0242 | 1000 | 1.4084 | - | - |
0.0363 | 1500 | 0.7062 | - | - |
0.0484 | 2000 | 0.5236 | - | - |
0.0605 | 2500 | 0.4348 | - | - |
0.0727 | 3000 | 0.3657 | - | - |
0.0848 | 3500 | 0.3657 | - | - |
0.0969 | 4000 | 0.2952 | - | - |
0.1090 | 4500 | 0.3805 | - | - |
0.1211 | 5000 | 0.3255 | - | - |
0.1332 | 5500 | 0.2621 | - | - |
0.1453 | 6000 | 0.2377 | - | - |
0.1574 | 6500 | 0.2139 | - | - |
0.1695 | 7000 | 0.2085 | - | - |
0.1816 | 7500 | 0.1809 | - | - |
0.1937 | 8000 | 0.1711 | - | - |
0.2059 | 8500 | 0.1608 | - | - |
0.2180 | 9000 | 0.1808 | - | - |
0.2301 | 9500 | 0.1553 | - | - |
0.2422 | 10000 | 0.1417 | - | - |
0.2543 | 10500 | 0.1329 | - | - |
0.2664 | 11000 | 0.1689 | - | - |
0.2785 | 11500 | 0.1292 | - | - |
0.2906 | 12000 | 0.1181 | - | - |
0.3027 | 12500 | 0.1223 | - | - |
0.3148 | 13000 | 0.129 | - | - |
0.3269 | 13500 | 0.0911 | - | - |
0.3391 | 14000 | 0.113 | - | - |
0.3512 | 14500 | 0.0955 | - | - |
0.3633 | 15000 | 0.108 | - | - |
0.3754 | 15500 | 0.094 | - | - |
0.3875 | 16000 | 0.0947 | - | - |
0.3996 | 16500 | 0.0748 | - | - |
0.4117 | 17000 | 0.0699 | - | - |
0.4238 | 17500 | 0.0707 | - | - |
0.4359 | 18000 | 0.0768 | - | - |
0.4480 | 18500 | 0.0805 | - | - |
0.4601 | 19000 | 0.0705 | - | - |
0.4723 | 19500 | 0.069 | - | - |
0.4844 | 20000 | 0.072 | - | - |
0.4965 | 20500 | 0.0669 | - | - |
0.5086 | 21000 | 0.066 | - | - |
0.5207 | 21500 | 0.0624 | - | - |
0.5328 | 22000 | 0.0687 | - | - |
0.5449 | 22500 | 0.076 | - | - |
0.5570 | 23000 | 0.0563 | - | - |
0.5691 | 23500 | 0.0594 | - | - |
0.5812 | 24000 | 0.0524 | - | - |
0.5933 | 24500 | 0.0528 | - | - |
0.6055 | 25000 | 0.0448 | - | - |
0.6176 | 25500 | 0.041 | - | - |
0.6297 | 26000 | 0.0397 | - | - |
0.6418 | 26500 | 0.0489 | - | - |
0.6539 | 27000 | 0.0595 | - | - |
0.6660 | 27500 | 0.034 | - | - |
0.6781 | 28000 | 0.0569 | - | - |
0.6902 | 28500 | 0.0467 | - | - |
0.7023 | 29000 | 0.0323 | - | - |
0.7144 | 29500 | 0.0428 | - | - |
0.7266 | 30000 | 0.0344 | - | - |
0.7387 | 30500 | 0.029 | - | - |
0.7508 | 31000 | 0.0418 | - | - |
0.7629 | 31500 | 0.0285 | - | - |
0.7750 | 32000 | 0.0425 | - | - |
0.7871 | 32500 | 0.0266 | - | - |
0.7992 | 33000 | 0.0325 | - | - |
0.8113 | 33500 | 0.0215 | - | - |
0.8234 | 34000 | 0.0316 | - | - |
0.8355 | 34500 | 0.0286 | - | - |
0.8476 | 35000 | 0.0285 | - | - |
0.8598 | 35500 | 0.0284 | - | - |
0.8719 | 36000 | 0.0147 | - | - |
0.8840 | 36500 | 0.0217 | - | - |
0.8961 | 37000 | 0.0311 | - | - |
0.9082 | 37500 | 0.0202 | - | - |
0.9203 | 38000 | 0.0236 | - | - |
0.9324 | 38500 | 0.0201 | - | - |
0.9445 | 39000 | 0.0246 | - | - |
0.9566 | 39500 | 0.0177 | - | - |
0.9687 | 40000 | 0.0173 | - | - |
0.9808 | 40500 | 0.0202 | - | - |
0.9930 | 41000 | 0.017 | - | - |
1.0 | 41291 | - | 0.0140 | 0.9991 |
Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.0
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Accelerate: 0.28.0
- Datasets: 2.18.0
- Tokenizers: 0.15.2
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",
}
TripletLoss
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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Model tree for slimaneMakh/triplet_CloseHlabel_farLabel_andnegativ-1M-5eps-XLMR_29may
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FacebookAI/xlm-roberta-base
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Evaluation results
- Cosine Accuracy on Unknownself-reported0.999
- Dot Accuracy on Unknownself-reported0.002
- Manhattan Accuracy on Unknownself-reported0.998
- Euclidean Accuracy on Unknownself-reported0.999
- Max Accuracy on Unknownself-reported0.999