slovakbert-skquad-mnlr

SentenceTransformer based on gerulata/slovakbert

Model Sources

• Repository: Model Training and Evaluation Scripts

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: gerulata/slovakbert <!-- at revision 629d4e16f546fad0054b5143fe13ccbea03259e2 -->
• Maximum Sequence Length: 300 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Prvý most cez Zlatý roh nechal vybudovať cisár Justinián I. V roku 1502 vypísal sultán Bajazid II. súťaž na stavbu nového mosta, do ktorej sa prihlásili aj Leonardo da Vinci a Michelangelo Buonarroti, ale z realizácie návrhov nakoniec zišlo. V roku 1863 vznikol druhý, drevený most, ktorý v roku 1875 nahradil železný most, postavený francúzskymi staviteľmi. Štvrtý most postavili Nemci v roku 1912 a slúžil až do roku 1992, kedy bol zničený požiarom. Bolo rozhodnuté o stavbe mosta súčasného, ktorý vybudovala domáca firma STFA Group.',
    'V ktorom roku vznikol druhý drevený most cez záliv Zlatý roh ?',
    'Aká je priemerná dĺžka života v Eritrei ?',
]
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.* --> <!-- ## 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: 65,699 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	sentence_2
  type	string	string	string
  details	<ul><li>min: 99 tokens</li><li>mean: 185.5 tokens</li><li>max: 300 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 34 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 15.05 tokens</li><li>max: 34 tokens</li></ul>

• Samples:

  sentence_0	sentence_1	sentence_2
  <code>Gymnázium a neskôr filozofiu študoval v Nitre. V roku 1951 ilegálne emigroval cez Rakúsko do Nemecka, kde v St. Augustine skončil teologické štúdiá. V roku 1952 bol vysvätený za kňaza a následný rok odchádza ako misionár do mesta Bello Horizonte v Brazílii. Páter Jozef Filus pôsobil v tejto krajine celých 46 rokov. Tu sa učil po portugalsky, dejiny a kultúru krajiny. Neskôr pôsobil v mestách Tres Rios a Rio de Janeiro, Santa Casa, Juiz Fora, Vale Jequitiuhonha a Gama. Ešte aj vo svojich 75 rokoch pôsobil vo veľkej nemocnici v Bello Horizonte. V tomto meste je aj pochovaný.</code>	<code>V ktorom roku bol rímskokatolícky misionár Jozef Filus vysvätený za kňaza ?</code>	<code>V ktorom roku nebol rímskokatolícky misionár Jozef Filus vysvätený za kňaza ?</code>
  <code>Gymnázium a neskôr filozofiu študoval v Nitre. V roku 1951 ilegálne emigroval cez Rakúsko do Nemecka, kde v St. Augustine skončil teologické štúdiá. V roku 1952 bol vysvätený za kňaza a následný rok odchádza ako misionár do mesta Bello Horizonte v Brazílii. Páter Jozef Filus pôsobil v tejto krajine celých 46 rokov. Tu sa učil po portugalsky, dejiny a kultúru krajiny. Neskôr pôsobil v mestách Tres Rios a Rio de Janeiro, Santa Casa, Juiz Fora, Vale Jequitiuhonha a Gama. Ešte aj vo svojich 75 rokoch pôsobil vo veľkej nemocnici v Bello Horizonte. V tomto meste je aj pochovaný.</code>	<code>Kam emigroval rímskokatolícky misionár Jozef Filus v roku 1951 ?</code>	<code>Kam emigroval rímskokatolícky misionár Jozef Filus v roku 2001 ?</code>
  <code>Gymnázium a neskôr filozofiu študoval v Nitre. V roku 1951 ilegálne emigroval cez Rakúsko do Nemecka, kde v St. Augustine skončil teologické štúdiá. V roku 1952 bol vysvätený za kňaza a následný rok odchádza ako misionár do mesta Bello Horizonte v Brazílii. Páter Jozef Filus pôsobil v tejto krajine celých 46 rokov. Tu sa učil po portugalsky, dejiny a kultúru krajiny. Neskôr pôsobil v mestách Tres Rios a Rio de Janeiro, Santa Casa, Juiz Fora, Vale Jequitiuhonha a Gama. Ešte aj vo svojich 75 rokoch pôsobil vo veľkej nemocnici v Bello Horizonte. V tomto meste je aj pochovaný.</code>	<code>Kde študoval rímskokatolícky misionár Jozef Filus filozofiu ?</code>	<code>Kde študoval rímskokatolícky misionár Jozef Filus medicínu ?</code>

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

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 1
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• 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: 2
• 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: batch_sampler
• multi_dataset_batch_sampler: round_robin

</details>

Training Logs

Framework Versions

• Python: 3.10.8
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 1.13.1
• Accelerate: 0.31.0
• Datasets: 2.19.1
• 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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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