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

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 Type: Sentence Transformer
• Base model: Davlan/bert-base-multilingual-cased-finetuned-yoruba <!-- at revision 000f80b4509f73bca9a33f9db0573d6f67396a12 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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: 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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Triplet

• Evaluated with <code>TripletEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 5,019 training samples
• Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
• Approximate statistics based on the first 1000 samples:

  	query	pos	neg
  type	string	string	string
  details	<ul><li>min: 5 tokens</li><li>mean: 24.62 tokens</li><li>max: 74 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 24.14 tokens</li><li>max: 79 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 25.71 tokens</li><li>max: 98 tokens</li></ul>

• Samples:

  query	pos	neg
  <code>Kini idi ti Ilu India ṣe a ko ni ọkan lori ijiroro oloselu kan bi ni AMẸRIKA?</code>	<code>Kini idi ti a ko le ni ijiroro gbangba laarin awọn oloselu ni India bi ọkan ninu wa?</code>	<code>Njẹ eniyan le da quo duro de India Pakistan ariyanjiyan?A ni aisan ati ti o ri eyi lojoojumọ ni olopo?</code>
  <code>Kini OnePlus Ọkan?</code>	<code>Bawo ni OnePlus kan?</code>	<code>Kini idi ti OnePlus Ọkan dara?</code>
  <code>Ṣe ọkan wa ṣe iṣakoso awọn ẹdun wa?</code>	<code>Bawo ni ọlọgbọn ati awọn eniyan aṣeyọri ṣe ṣakoso awọn ẹdun wọn?</code>	<code>Bawo ni MO ṣe le ṣakoso awọn ẹdun mi rere fun awọn eniyan ti Mo nifẹ ṣugbọn wọn ko bikita nipa mi?</code>

• Loss: <code>TripletLoss</code> with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 1,000 evaluation samples
• Columns: <code>query</code>, <code>pos</code>, and <code>neg</code>
• Approximate statistics based on the first 1000 samples:

  	query	pos	neg
  type	string	string	string
  details	<ul><li>min: 6 tokens</li><li>mean: 24.32 tokens</li><li>max: 94 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 24.06 tokens</li><li>max: 115 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 25.58 tokens</li><li>max: 121 tokens</li></ul>

• Samples:

  query	pos	neg
  <code>Bawo ni o jẹ ọjọ ebi?</code>	<code>Bawo ni o jẹ ọsan</code>	<code>Njẹ NEBM lueMo ṣẹlẹ lati wa awọn ifiweranṣẹ ti o sọ pe o jẹ iro ati pe ko ni itter</code>
  <code>Kini awọn ohun elo akọkọ ti kọnputa kan?</code>	<code>Kini diẹ ninu awọn ẹya akọkọ ti kọnputa kan?Awọn iṣẹ wo ni wọn nṣe iranṣẹ?</code>	<code>Kini awọn eto kọmputa?Kini awọn iṣẹ ti awọn eto kọnputa?</code>
  <code>Ṣe o le faffiti Artists fun sokiri Graffiti ni Rockdale County, GA?</code>	<code>Ṣe o le fun awọn ojukokoro fun fun sokiri Graffiti ni Cockdale County, Georgia?</code>	<code>Kini idi ti Graffiti jẹ arufin?</code>

• Loss: <code>TripletLoss</code> 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

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

• 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

</details>

Training Logs

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}
}

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