Update README.md

README.md

@@ -35,410 +35,3 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [k
 - **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.',
-    
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [, 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.*
--->
-
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-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-## Model Card Contact
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