amDANIEL2024/tulati-v1-afrie5-salt

SentenceTransformer based on McGill-NLP/AfriE5-Large-instruct.

This is a sentence-transformers model finetuned from McGill-NLP/AfriE5-Large-instruct. It maps inputs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details: Tulati is a bi-encoder finetuned using the SALT Dataset

Model Description

• —Model Type: Sentence Transformer
• —Base model: McGill-NLP/AfriE5-Large-instruct <!-- at revision 0d7a51373bacc776c5f41696116c6c9100dcde72 -->
• —Maximum Sequence Length: 64 tokens
• —Output Dimensionality: 1024 dimensions
• —Similarity Function: Cosine Similarity
• —Supported Modality: Text <!-- - 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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
)

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("amDANIEL2024/tulati-v1-afrie5-salt")
# Run inference
sentences = [
    'Instruct: Retrieve sentences that are semantically consistent with the input.\nQuery: How many books do you want?',
    'Instruct: Retrieve sentences that are semantically consistent with the input.\nQuery: Imito buk adi?',
    'Instruct: Retrieve sentences that are semantically consistent with the input.\nQuery: Kot okwero gonye pi tutunu.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.8440, -0.0016],
#         [ 0.8440,  1.0000,  0.0296],
#         [-0.0016,  0.0296,  1.0000]])

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 119,734 training samples
• —Columns: <code>anchor</code> and <code>positive</code>
• —Approximate statistics based on the first 100 samples: | | anchor | positive | |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | <ul><li>min: 27 tokens</li><li>mean: 33.02 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 41.24 tokens</li><li>max: 61 tokens</li></ul> |
• —Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Instruct: Retrieve sentences that are semantically consistent with the input.<br>Query: He appealed to the Supreme Court to be given bail.</code> | <code>Instruct: Retrieve sentences that are semantically consistent with the input.<br>Query: Yajulira mu kkooti ensukkulumu ateebwe ku kakalu ka kkooti.</code> | | <code>Instruct: Retrieve sentences that are semantically consistent with the input.<br>Query: People sell goods to get money.</code> | <code>Instruct: Retrieve sentences that are semantically consistent with the input.<br>Query: Dano cato wil me nongo cente.</code> | | <code>Instruct: Retrieve sentences that are semantically consistent with the input.<br>Query: The move was aimed at rewarding the company's customers.</code> | <code>Instruct: Retrieve sentences that are semantically consistent with the input.<br>Query: Ekigyendererwa kikaba kiri okuheereza abashagiki ba kampuni akasiimo.</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 16
• —learning_rate: 2e-05
• —num_train_epochs: 1
• —warmup_steps: 0.1
• —fp16: True
• —dataloader_drop_last: True
• —batch_sampler: no_duplicates

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: 1
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 2e-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: 1
• —max_steps: -1
• —lr_scheduler_type: linear
• —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: True
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —local_rank: -1
• —ddp_backend: None
• —debug: []
• —dataloader_drop_last: True
• —dataloader_num_workers: 0
• —dataloader_prefetch_factor: None
• —disable_tqdm: False
• —remove_unused_columns: True
• —label_names: None
• —load_best_model_at_end: False
• —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: no_duplicates
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Training Time

• —Training: 2.4 hours

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 6.1.0
• —Transformers: 5.0.0
• —PyTorch: 2.10.0+cu128
• —Accelerate: 1.15.0
• —Datasets: 5.0.1
• —Tokenizers: 0.22.2

Additional Resources

• —Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
• —Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
• —Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
• —Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
• —Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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