Hugging Face's logo

adejumobi
/
bert-base-multilingual-cased-finetuned-yoruba-IR

Sentence Similarity
sentence-transformers
Safetensors
Yoruba
bert
feature-extraction
Generated from Trainer
dataset_size:5019
loss:TripletLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
bert-base-multilingual-cased-finetuned-yoruba-IR / README.md
adejumobi's picture
adejumobi
Update README.md
04724ff verified
preview code
|
raw
history blame contribute delete
19.3 kB
metadata
language:
  - yo
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:5019
  - loss:TripletLoss
base_model: Davlan/bert-base-multilingual-cased-finetuned-yoruba
datasets:
  - embedding-data/QQP_triplets
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
widget:
  - source_sentence: Bawo ni eniyan lasan ṣe le ṣe agbaye ni aye ti o dara julọ?
    sentences:
      - Ewo ni fiimu ti o dara julọ ti agbaye?
      - >-
        Bawo ni a ṣe le ṣe agbaye ni aye ti o dara julọ fun gbogbo ati fun iran
        iwaju lati wa?
      - Njẹ aiye yii dara julọ tabi buru?
  - source_sentence: Ni Pokemon ati tẹmpili ti okun, kilode ti o yanilenu Manicy?
    sentences:
      - Kini idi ti Manafy ọmọ-ọwọ ni Pokémon ger ati tẹmpili ti okun?
      - Bawo ni awọn ibeere mi ṣe wa nigbagbogbo nigbagbogbo lori Quora?
      - Ṣe "Pokémon ti o wuyi ati tẹmpili ti Okun" ka akọku?
  - source_sentence: Kini itumo igbesi aye yii?
    sentences:
      - Kini "Gbe igbesi aye rẹ" tumọ si?
      - Kini o ro pe o jẹ itumọ ti igbesi aye?
      - >-
        Nitorinaa bawo ni MO ṣe le gba meth lati fulu jade ninu ara ni awọn
        wakati 2 ṣaaju idanwo togbo kan?
  - source_sentence: >-
      Nibo ni MO le gba ọpọlọpọ awọn aso deede, awọn aṣọ alekun & awọn aṣọ irọlẹ
      ni goolu ni eti okun?
    sentences:
      - >-
        Nibo ni MO le gba ọpọlọpọ awọn awọ ati titobi fun awọn aṣọ awọn alagbaje
        ni Gold Coast?
      - >-
        Kini yoo ṣẹlẹ ti o ba jẹ ki o dina nkan bi Facebook tabi Google ni
        isansa ti iṣan neta?
      - Nibo ni MO le gba ikojọpọ ti o lẹwa fun awọn aṣọ igbeyawo ni Sydney?
  - source_sentence: Kini o yẹ ki Ilu India ṣe lori ikọlu UI?
    sentences:
      - Bawo ni MO ṣe sọ Gẹẹsi leta ni ifọrọwanilẹnuwo kan?
      - >-
        Lẹhin gbogbo họọsi ti media media ti ṣẹda awọn ikọlu URI Wip, kii yoo jẹ
        ohun itiju fun India ti ko ba kọlu Pakistan?
      - Bawo ni India le dahun si ikọlu ẹru UI?
pipeline_tag: sentence-similarity
model-index:
  - name: >-
      SentenceTransformer based on
      Davlan/bert-base-multilingual-cased-finetuned-yoruba
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.865
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.135
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.868
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.868
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.868
            name: Max Accuracy

SentenceTransformer based on Davlan/bert-base-multilingual-cased-finetuned-yoruba

This is a sentence-transformers model finetuned from Davlan/bert-base-multilingual-cased-finetuned-yoruba. 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 Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("adejumobi/bert-base-multilingual-cased-finetuned-yoruba-IR")
# Run inference
sentences = [
    'Kini o yẹ ki Ilu India ṣe lori ikọlu UI?',
    'Bawo ni India le dahun si ikọlu ẹru UI?',
    'Lẹhin gbogbo họọsi ti media media ti ṣẹda awọn ikọlu URI Wip, kii yoo jẹ ohun itiju fun India ti ko ba kọlu Pakistan?',
]
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

Metric Value
cosine_accuracy 0.865
dot_accuracy 0.135
manhattan_accuracy 0.868
euclidean_accuracy 0.868
max_accuracy 0.868

Training Details

Training Dataset

Unnamed Dataset

  • Size: 5,019 training samples
  • Columns: query, pos, and neg
  • Approximate statistics based on the first 1000 samples:
    query pos neg
    type string string string
    details
    • min: 5 tokens
    • mean: 24.62 tokens
    • max: 74 tokens
    • min: 6 tokens
    • mean: 24.14 tokens
    • max: 79 tokens
    • min: 4 tokens
    • mean: 25.71 tokens
    • max: 98 tokens
  • Samples:
    query pos neg
    Kini idi ti Ilu India ṣe a ko ni ọkan lori ijiroro oloselu kan bi ni AMẸRIKA? Kini idi ti a ko le ni ijiroro gbangba laarin awọn oloselu ni India bi ọkan ninu wa? Njẹ eniyan le da quo duro de India Pakistan ariyanjiyan?A ni aisan ati ti o ri eyi lojoojumọ ni olopo?
    Kini OnePlus Ọkan? Bawo ni OnePlus kan? Kini idi ti OnePlus Ọkan dara?
    Ṣe ọkan wa ṣe iṣakoso awọn ẹdun wa? Bawo ni ọlọgbọn ati awọn eniyan aṣeyọri ṣe ṣakoso awọn ẹdun wọn? Bawo ni MO ṣe le ṣakoso awọn ẹdun mi rere fun awọn eniyan ti Mo nifẹ ṣugbọn wọn ko bikita nipa mi?
  • Loss: TripletLoss with these parameters:
    {
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Evaluation Dataset

Unnamed Dataset

  • Size: 1,000 evaluation samples
  • Columns: query, pos, and neg
  • Approximate statistics based on the first 1000 samples:
    query pos neg
    type string string string
    details
    • min: 6 tokens
    • mean: 24.32 tokens
    • max: 94 tokens
    • min: 6 tokens
    • mean: 24.06 tokens
    • max: 115 tokens
    • min: 6 tokens
    • mean: 25.58 tokens
    • max: 121 tokens
  • Samples:
    query pos neg
    Bawo ni o jẹ ọjọ ebi? Bawo ni o jẹ ọsan Njẹ NEBM lueMo ṣẹlẹ lati wa awọn ifiweranṣẹ ti o sọ pe o jẹ iro ati pe ko ni itter
    Kini awọn ohun elo akọkọ ti kọnputa kan? Kini diẹ ninu awọn ẹya akọkọ ti kọnputa kan?Awọn iṣẹ wo ni wọn nṣe iranṣẹ? Kini awọn eto kọmputa?Kini awọn iṣẹ ti awọn eto kọnputa?
    Ṣe o le faffiti Artists fun sokiri Graffiti ni Rockdale County, GA? Ṣe o le fun awọn ojukokoro fun fun sokiri Graffiti ni Cockdale County, Georgia? Kini idi ti Graffiti jẹ arufin?
  • Loss: TripletLoss with these parameters:
    {
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 12
  • per_device_eval_batch_size: 3
  • learning_rate: 1e-05
  • num_train_epochs: 5
  • warmup_ratio: 0.1
  • fp16: True
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 12
  • per_device_eval_batch_size: 3
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • learning_rate: 1e-05
  • weight_decay: 0.0
  • 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: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • 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
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • dispatch_batches: None
  • split_batches: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss loss cosine_accuracy
0 0 - - 0.827
0.2387 100 4.247 3.6056 0.815
0.4773 200 3.3576 2.7548 0.809
0.7160 300 2.931 2.3805 0.843
0.9547 400 2.4476 2.1895 0.858
1.1933 500 2.5839 2.1148 0.854
1.4320 600 2.0645 2.0497 0.855
1.6706 700 1.8386 2.0328 0.847
1.9093 800 1.5527 1.9380 0.857
2.1480 900 1.7298 1.8999 0.861
2.3866 1000 1.4375 1.8744 0.855
2.6253 1100 1.1605 1.8761 0.861
2.8640 1200 1.0601 1.8658 0.862
3.1026 1300 1.1019 1.8181 0.861
3.3413 1400 1.052 1.8088 0.854
3.5800 1500 0.8807 1.7937 0.862
3.8186 1600 0.7877 1.7963 0.862
4.0573 1700 0.7613 1.7869 0.868
4.2959 1800 0.8018 1.7696 0.867
4.5346 1900 0.6717 1.7815 0.865
4.7733 2000 0.6603 1.7776 0.865

Framework Versions

  • Python: 3.10.13
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.2
  • PyTorch: 2.1.2
  • Accelerate: 0.31.0
  • Datasets: 2.19.2
  • Tokenizers: 0.19.1

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}
}