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README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:500000
+- loss:CosineSimilarityLoss
+base_model: kinit/slovakbert-sts-stsb
+widget:
+- source_sentence: V súlade so zisteným skutkovým stavom a citovaným ustanovením zákona
+    súd návrhu vyhovel.
+  sentences:
+  - V súlade so zisteným skutkovým stavom a citovaným ustanovením zákona súd návrhu
+    vyhovel.
+  - 13C/88/2013, rozsudok Okresného súdu Revúca sp.zn.
+  - Výrok o trovách konania bol odôvodnený ustanovením § 142 ods.
+- source_sentence: '2013 tak, aby sa do školenia vmestil praktický
    nácvik (č.l.'
+  sentences:
+  - '2013 tak, aby sa do školenia vmestil praktický
    nácvik (č.l.'
+  - 'Povinnosť súdu posúdiť
    uplatnený nárok aj z hľadiska súladu s dobrými mravmi je zvýraznená v tých prípadoch,
+    keď ich účastník
    konania vyslovene namieta, musí však na ňu prihliadnuť aj v prípadoch, kedy takýto
+    rozpor zistí súd
    sám pri prieskume ex officio, keďže na absolútnu neplatnosť úkonu súd musí prihliadať
+    ex lege.'
+  - 'Nie je preto možné súhlasiť
    s názorom súdu prvého stupňa, že sú závislé na úverovom vzťahu.'
+- source_sentence: 'Žalobca je povinný nahradiť vedľajšiemu účastníkovi na účet jeho
+    právneho zástupcu trovy odvolacieho
    konania vo výške 59,84 eur do 3 dní od právoplatnosti tohto rozsudku.'
+  sentences:
+  - 'Zdôraznil, že navrhovateľ
    skutočnosti o existencii iného vzťahu medzi pôvodným majiteľom zmenky a odporcom
+    vôbec netvrdil,



    teda nebol nositeľom dôkazného bremena.'
+  - 'Žalobca je povinný nahradiť vedľajšiemu účastníkovi na účet jeho právneho zástupcu
+    trovy odvolacieho
    konania vo výške 59,84 eur do 3 dní od právoplatnosti tohto rozsudku.'
+  - 'Za týmto účelom žiadal žalobcu o doplnenie podaného návrhu,
    aby bolo možné posúdiť skutočnosť, či ide o uplatnenie práv zo spotrebiteľského
+    vzťahu, alebo nie.'
+- source_sentence: 'Dátum začiatku úročenia zmenkovej sumy vyplní na
    zmenke remitent tak, že ním bude deň, kedy sa v zmysle bodu 4 týchto podmienok
+    stal splatný celý dlh.'
+  sentences:
+  - 'Kumulatívne s tým musí byť splnená podmienka, že pohľadávka sa nezdá byť zjavne
+    neopodstatnená,
    alebo návrh neprípustný.'
+  - 2 veta druhá O.s.p.
+  - 'Dátum začiatku úročenia zmenkovej sumy vyplní na
    zmenke remitent tak, že ním bude deň, kedy sa v zmysle bodu 4 týchto podmienok
+    stal splatný celý dlh.'
+- source_sentence: 'Ustanovenie tohto odseku platí aj v prípade zmeny majiteľa zmenky
+    alebo postúpenia práva zo
    zmenky.'
+  sentences:
+  - 'Ustanovenie tohto odseku platí aj v prípade zmeny majiteľa zmenky alebo postúpenia
+    práva zo
    zmenky.'
+  - 'Miroslav Šedivec
    ECLI: ECLI:SK:OSRA:2016:6816204309.1

    Uznesenie
    Okresný súd Revúca vo veci

    navrhovateľa: Dopravný podnik mesta Košice, a.s., IČO: 31701914
    Bardejovská 6
    043 29 Košice

    zast.'
+  - '3 (ktorému súdu je
    určené, kto ho robí, ktorej veci sa týka a čo sleduje, jeho datovania a podpísania)
+    uviesť, proti ktorému
    rozhodnutiu smeruje, v akom rozsahu sa napáda, v čom sa toto rozhodnutie alebo
+    postup súdu považuje
    za nesprávny a čoho sa odvolateľ domáha.'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on kinit/slovakbert-sts-stsb
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [kinit/slovakbert-sts-stsb](https://huggingface.co/kinit/slovakbert-sts-stsb). 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:** [kinit/slovakbert-sts-stsb](https://huggingface.co/kinit/slovakbert-sts-stsb) <!-- at revision 770633080dda1d1867e7179a456ed53138280c08 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, '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:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Ustanovenie tohto odseku platí aj v prípade zmeny majiteľa zmenky alebo postúpenia práva zo\u2028zmenky.',
+    'Ustanovenie tohto odseku platí aj v prípade zmeny majiteľa zmenky alebo postúpenia práva zo\u2028zmenky.',
+    '3 (ktorému súdu je\u2028určené, kto ho robí, ktorej veci sa týka a čo sleduje, jeho datovania a podpísania) uviesť, proti ktorému\u2028rozhodnutiu smeruje, v akom rozsahu sa napáda, v čom sa toto rozhodnutie alebo postup súdu považuje\u2028za nesprávny a čoho sa odvolateľ domáha.',
+]
+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: 500,000 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         | label                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                         |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 31.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 31.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                        | sentence_1                                                                                                                                        | label            |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>
  Súd: Okresný súd Námestovo
  Spisová značka: 5C/265/2015
  Identifikačné číslo súdneho spisu: 5815205480
  Dátum vydania rozhodnutia: 02.</code> | <code>
  Súd: Okresný súd Námestovo
  Spisová značka: 5C/265/2015
  Identifikačné číslo súdneho spisu: 5815205480
  Dátum vydania rozhodnutia: 02.</code> | <code>1.0</code> |
+  | <code>06.</code>                                                                                                                                  | <code>06.</code>                                                                                                                                  | <code>1.0</code> |
+  | <code>2016
  Meno a priezvisko sudcu, VSÚ: JUDr.</code>                                                                                             | <code>2016
  Meno a priezvisko sudcu, VSÚ: JUDr.</code>                                                                                             | <code>1.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `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`: 4
+- `per_device_eval_batch_size`: 4
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_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`: 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}
+- `tp_size`: 0
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch | Step  | Training Loss |
+|:-----:|:-----:|:-------------:|
+| 0.008 | 500   | 0.0089        |
+| 0.016 | 1000  | 0.0001        |
+| 0.024 | 1500  | 0.0           |
+| 0.032 | 2000  | 0.0           |
+| 0.04  | 2500  | 0.0           |
+| 0.048 | 3000  | 0.0           |
+| 0.056 | 3500  | 0.0           |
+| 0.064 | 4000  | 0.0           |
+| 0.072 | 4500  | 0.0           |
+| 0.08  | 5000  | 0.0           |
+| 0.088 | 5500  | 0.0           |
+| 0.096 | 6000  | 0.0           |
+| 0.104 | 6500  | 0.0           |
+| 0.112 | 7000  | 0.0           |
+| 0.12  | 7500  | 0.0           |
+| 0.128 | 8000  | 0.0           |
+| 0.136 | 8500  | 0.0           |
+| 0.144 | 9000  | 0.0           |
+| 0.152 | 9500  | 0.0           |
+| 0.16  | 10000 | 0.0           |
+| 0.168 | 10500 | 0.0           |
+| 0.176 | 11000 | 0.0           |
+| 0.184 | 11500 | 0.0           |
+| 0.192 | 12000 | 0.0           |
+| 0.2   | 12500 | 0.0           |
+| 0.208 | 13000 | 0.0           |
+| 0.216 | 13500 | 0.0           |
+| 0.224 | 14000 | 0.0           |
+| 0.232 | 14500 | 0.0           |
+| 0.24  | 15000 | 0.0           |
+| 0.248 | 15500 | 0.0           |
+| 0.256 | 16000 | 0.0           |
+| 0.264 | 16500 | 0.0           |
+| 0.272 | 17000 | 0.0           |
+| 0.28  | 17500 | 0.0           |
+| 0.288 | 18000 | 0.0           |
+| 0.296 | 18500 | 0.0           |
+| 0.304 | 19000 | 0.0           |
+| 0.312 | 19500 | 0.0           |
+| 0.32  | 20000 | 0.0           |
+| 0.328 | 20500 | 0.0           |
+| 0.336 | 21000 | 0.0           |
+| 0.344 | 21500 | 0.0           |
+| 0.352 | 22000 | 0.0           |
+| 0.36  | 22500 | 0.0           |
+| 0.368 | 23000 | 0.0           |
+| 0.376 | 23500 | 0.0           |
+| 0.384 | 24000 | 0.0           |
+| 0.392 | 24500 | 0.0           |
+| 0.4   | 25000 | 0.0           |
+| 0.408 | 25500 | 0.0           |
+| 0.416 | 26000 | 0.0           |
+| 0.424 | 26500 | 0.0           |
+| 0.432 | 27000 | 0.0           |
+| 0.44  | 27500 | 0.0           |
+| 0.448 | 28000 | 0.0           |
+| 0.456 | 28500 | 0.0           |
+| 0.464 | 29000 | 0.0           |
+| 0.472 | 29500 | 0.0           |
+| 0.48  | 30000 | 0.0           |
+| 0.488 | 30500 | 0.0           |
+| 0.496 | 31000 | 0.0           |
+| 0.504 | 31500 | 0.0           |
+| 0.512 | 32000 | 0.0           |
+| 0.52  | 32500 | 0.0           |
+| 0.528 | 33000 | 0.0           |
+| 0.536 | 33500 | 0.0           |
+| 0.544 | 34000 | 0.0           |
+| 0.552 | 34500 | 0.0           |
+| 0.56  | 35000 | 0.0           |
+| 0.568 | 35500 | 0.0           |
+| 0.576 | 36000 | 0.0           |
+| 0.584 | 36500 | 0.0           |
+| 0.592 | 37000 | 0.0           |
+| 0.6   | 37500 | 0.0           |
+| 0.608 | 38000 | 0.0           |
+| 0.616 | 38500 | 0.0           |
+| 0.624 | 39000 | 0.0           |
+| 0.632 | 39500 | 0.0           |
+| 0.64  | 40000 | 0.0           |
+| 0.648 | 40500 | 0.0           |
+| 0.656 | 41000 | 0.0           |
+| 0.664 | 41500 | 0.0           |
+| 0.672 | 42000 | 0.0           |
+| 0.68  | 42500 | 0.0           |
+| 0.688 | 43000 | 0.0           |
+| 0.696 | 43500 | 0.0           |
+| 0.704 | 44000 | 0.0           |
+| 0.712 | 44500 | 0.0           |
+| 0.72  | 45000 | 0.0           |
+| 0.728 | 45500 | 0.0           |
+| 0.736 | 46000 | 0.0           |
+| 0.744 | 46500 | 0.0           |
+| 0.752 | 47000 | 0.0           |
+| 0.76  | 47500 | 0.0           |
+| 0.768 | 48000 | 0.0           |
+| 0.776 | 48500 | 0.0           |
+| 0.784 | 49000 | 0.0           |
+| 0.792 | 49500 | 0.0           |
+| 0.8   | 50000 | 0.0           |
+| 0.808 | 50500 | 0.0           |
+| 0.816 | 51000 | 0.0           |
+| 0.824 | 51500 | 0.0           |
+| 0.832 | 52000 | 0.0           |
+| 0.84  | 52500 | 0.0           |
+| 0.848 | 53000 | 0.0           |
+| 0.856 | 53500 | 0.0           |
+| 0.864 | 54000 | 0.0           |
+| 0.872 | 54500 | 0.0           |
+| 0.88  | 55000 | 0.0           |
+| 0.888 | 55500 | 0.0           |
+| 0.896 | 56000 | 0.0           |
+| 0.904 | 56500 | 0.0           |
+| 0.912 | 57000 | 0.0           |
+| 0.92  | 57500 | 0.0           |
+| 0.928 | 58000 | 0.0           |
+| 0.936 | 58500 | 0.0           |
+| 0.944 | 59000 | 0.0           |
+| 0.952 | 59500 | 0.0           |
+| 0.96  | 60000 | 0.0           |
+| 0.968 | 60500 | 0.0           |
+| 0.976 | 61000 | 0.0           |
+| 0.984 | 61500 | 0.0           |
+| 0.992 | 62000 | 0.0           |
+| 1.0   | 62500 | 0.0           |
+
+</details>
+
+### Framework Versions
+- Python: 3.9.13
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50263": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 256,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}