trained on the initial 100k + 100k

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,507 @@
+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:300000
+- loss:DenoisingAutoEncoderLoss
+base_model: FacebookAI/roberta-base
+datasets: []
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: free in spain? Are Spain free Motorways toll-free Spain, renewing
+    old concessions coming
+  sentences:
+  - how to calculate weighted grade percentage in excel? To find the grade, multiply
+    the grade for each assignment against the weight, and then add these totals all
+    up. So for each cell (in the Total column) we will enter =SUM(Grade Cell * Weight
+    Cell), so my first formula is =SUM(B2*C2), the next one would be =SUM(B3*C3) and
+    so on.
+  - In Red Dead Redemption 2's story mode, players have to go to "Story" in the menu
+    and then click the save icon from there. However, in Red Dead Online, there is
+    no such option. On the contrary, players have no way to manually save their game,
+    which is pretty much par for the course in an online multiplayer experience.
+  - are motorways free in spain? Are motorways in Spain free? Motorways are 90% toll-free
+    in Spain. Since 2018, Spain isn't renewing old concessions coming to end.
+- source_sentence: things do fort wayne?
+  sentences:
+  - what is the difference between a z71 and a 4x4? A Z71 has more undercarriage protection
+    (more skid plates) and heavier duty shock absorbers/struts for off road use than
+    a 4X4. Other than that the two are pretty much the same.
+  - is suboxone bad for kidneys?
+  - indoor things to do in fort wayne indiana?
+- source_sentence: a should hair?
+  sentences:
+  - how many times in a week should you shampoo your hair?
+  - Sujith fell into the borewell on Friday around 5:45 pm while playing on the family's
+    farm. Initially, he was trapped at a depth of 26 feet but slipped to 88 feet during
+    attempts to pull him up by tying ropes around his hands. Sujith Wilson fell into
+    a borewell in Tamil Nadu's Trichy on Friday.
+  - how to calculate out retained earnings on balance sheet? The retained earnings
+    are calculated by adding net income to (or subtracting net losses from) the previous
+    term's retained earnings and then subtracting any net dividend(s) paid to the
+    shareholders. The figure is calculated at the end of each accounting period (quarterly/annually.)
+- source_sentence: long period does go
+  sentences:
+  - if someone blocked your email will you know? You could, indeed, be blocked It's
+    certainly possible that your recipient has blocked you. All that means is that
+    email from your email address is automatically discarded at that recipient's end.
+    You will not get a notification; there's simply no way to tell that this has happened.
+  - is drinking apple cider vinegar every day bad for you?
+  - how long after period does weight go down?
+- source_sentence: beer wine both sugar alcohol excessive be a infections You also
+    sweets, along with foods moldy cheese, if you prone.
+  sentences:
+  - how long does it take to get xfinity internet? Installation generally takes between
+    two to four hours.
+  - They began selling the plush animals to retailers rather than operating a store
+    themselves. Today, Boyds is a publicly traded company that manufactures 18 million-20
+    million bears a year, all at a government-owned facility in China.
+  - Since beer and wine both contain yeast and sugar (alcohol is sugar fermented by
+    yeast), excessive drinking can definitely be a recipe for yeast infections. You
+    should also go easy on sweets, along with foods like moldy cheese, mushrooms,
+    and anything fermented if you're prone to yeast infections. 3.
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on FacebookAI/roberta-base
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.6885553993934473
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.6912117328249255
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.6728262252927975
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.6724759418767672
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.6693578420498989
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.6690698040856067
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.18975985891617667
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.1786146878048478
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.6885553993934473
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.6912117328249255
+      name: Spearman Max
+---
+
+# SentenceTransformer based on FacebookAI/roberta-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base). 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:** [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) <!-- at revision e2da8e2f811d1448a5b465c236feacd80ffbac7b -->
+- **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](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': 512, '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("bobox/RoBERTa-base-unsupervised-TSDAE")
+# Run inference
+sentences = [
+    'beer wine both sugar alcohol excessive be a infections You also sweets, along with foods moldy cheese, if you prone.',
+    "Since beer and wine both contain yeast and sugar (alcohol is sugar fermented by yeast), excessive drinking can definitely be a recipe for yeast infections. You should also go easy on sweets, along with foods like moldy cheese, mushrooms, and anything fermented if you're prone to yeast infections. 3.",
+    'They began selling the plush animals to retailers rather than operating a store themselves. Today, Boyds is a publicly traded company that manufactures 18 million-20 million bears a year, all at a government-owned facility in China.',
+]
+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
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.6886     |
+| **spearman_cosine** | **0.6912** |
+| pearson_manhattan   | 0.6728     |
+| spearman_manhattan  | 0.6725     |
+| pearson_euclidean   | 0.6694     |
+| spearman_euclidean  | 0.6691     |
+| pearson_dot         | 0.1898     |
+| spearman_dot        | 0.1786     |
+| pearson_max         | 0.6886     |
+| spearman_max        | 0.6912     |
+
+<!--
+## 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: 300,000 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 19.88 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 46.45 tokens</li><li>max: 157 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                               | sentence_1                                                                                                                                                                                                                                                                                                                                                    |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>us have across domestic shorthair, a cat pedigreed one between two breeds Unlike domestic shorthairs which come in of looks, Shorthair kittens the distinct</code> | <code>Most of us have either lived with or come across a domestic shorthair, a cat that closely resembles the pedigreed American Shorthair. The one difference between the two breeds: Unlike domestic shorthairs, which come in a variety of looks, the American Shorthair produces kittens with the same distinct appearance.</code>                        |
+  | <code>much cost to get plugs normal with plugs, cost start $120 or if precious plugs are $150 to 200+ . 6 8 will price more required</code>                              | <code>how much does it cost to get your spark plugs changed? On a normal 4-cylinder engine with standard spark plugs, replacement cost can start around $120 up to $150+, or if precious metal spark plugs are required, $150 up to $200+. 6 cylinder and 8 Cylinder engines will increase in price, as more spark plugs are required.</code>                 |
+  | <code>much my paycheck state income%, your income level not tax rate you is of just that a flat tax rate, those, it has the</code>                                       | <code>how much taxes are taken out of my paycheck pa? Pennsylvania levies a flat state income tax rate of 3.07%. Therefore, your income level and filing status will not affect the income tax rate you pay at the state level. Pennsylvania is one of just eight states that has a flat income tax rate, and of those states, it has the lowest rate.</code> |
+* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 12
+- `per_device_eval_batch_size`: 12
+- `num_train_epochs`: 1
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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`: 12
+- `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`: False
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch | Step  | Training Loss | sts-test_spearman_cosine |
+|:-----:|:-----:|:-------------:|:------------------------:|
+| 0.02  | 500   | 7.1409        | -                        |
+| 0.04  | 1000  | 6.207         | -                        |
+| 0.05  | 1250  | -             | 0.6399                   |
+| 0.06  | 1500  | 5.8038        | -                        |
+| 0.08  | 2000  | 5.4963        | -                        |
+| 0.1   | 2500  | 5.2609        | 0.6799                   |
+| 0.12  | 3000  | 5.0997        | -                        |
+| 0.14  | 3500  | 5.0004        | -                        |
+| 0.15  | 3750  | -             | 0.7012                   |
+| 0.16  | 4000  | 4.8694        | -                        |
+| 0.18  | 4500  | 4.7805        | -                        |
+| 0.2   | 5000  | 4.6776        | 0.7074                   |
+| 0.22  | 5500  | 4.5757        | -                        |
+| 0.24  | 6000  | 4.4598        | -                        |
+| 0.25  | 6250  | -             | 0.7185                   |
+| 0.26  | 6500  | 4.3865        | -                        |
+| 0.28  | 7000  | 4.2692        | -                        |
+| 0.3   | 7500  | 4.2224        | 0.7205                   |
+| 0.32  | 8000  | 4.1347        | -                        |
+| 0.34  | 8500  | 4.0536        | -                        |
+| 0.35  | 8750  | -             | 0.7239                   |
+| 0.36  | 9000  | 4.0242        | -                        |
+| 0.38  | 9500  | 4.0193        | -                        |
+| 0.4   | 10000 | 3.9166        | 0.7153                   |
+| 0.42  | 10500 | 3.9004        | -                        |
+| 0.44  | 11000 | 3.8372        | -                        |
+| 0.45  | 11250 | -             | 0.7141                   |
+| 0.46  | 11500 | 3.8037        | -                        |
+| 0.48  | 12000 | 3.7788        | -                        |
+| 0.5   | 12500 | 3.7191        | 0.7078                   |
+| 0.52  | 13000 | 3.7036        | -                        |
+| 0.54  | 13500 | 3.6697        | -                        |
+| 0.55  | 13750 | -             | 0.7095                   |
+| 0.56  | 14000 | 3.6629        | -                        |
+| 0.58  | 14500 | 3.639         | -                        |
+| 0.6   | 15000 | 3.6048        | 0.7060                   |
+| 0.62  | 15500 | 3.6072        | -                        |
+| 0.64  | 16000 | 3.574         | -                        |
+| 0.65  | 16250 | -             | 0.7056                   |
+| 0.66  | 16500 | 3.5423        | -                        |
+| 0.68  | 17000 | 3.5379        | -                        |
+| 0.7   | 17500 | 3.5222        | 0.6969                   |
+| 0.72  | 18000 | 3.5076        | -                        |
+| 0.74  | 18500 | 3.5025        | -                        |
+| 0.75  | 18750 | -             | 0.6959                   |
+| 0.76  | 19000 | 3.4943        | -                        |
+| 0.78  | 19500 | 3.475         | -                        |
+| 0.8   | 20000 | 3.4874        | 0.6946                   |
+| 0.82  | 20500 | 3.4539        | -                        |
+| 0.84  | 21000 | 3.4704        | -                        |
+| 0.85  | 21250 | -             | 0.6942                   |
+| 0.86  | 21500 | 3.4689        | -                        |
+| 0.88  | 22000 | 3.4617        | -                        |
+| 0.9   | 22500 | 3.4471        | 0.6917                   |
+| 0.92  | 23000 | 3.4541        | -                        |
+| 0.94  | 23500 | 3.4394        | -                        |
+| 0.95  | 23750 | -             | 0.6915                   |
+| 0.96  | 24000 | 3.4505        | -                        |
+| 0.98  | 24500 | 3.4533        | -                        |
+| 1.0   | 25000 | 3.4574        | 0.6912                   |
+
+
+### 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
+```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",
+}
+```
+
+#### DenoisingAutoEncoderLoss
+```bibtex
+@inproceedings{wang-2021-TSDAE,
+    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
+    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", 
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
+    month = nov,
+    year = "2021",
+    address = "Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    pages = "671--688",
+    url = "https://arxiv.org/abs/2104.06979",
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "FacebookAI/roberta-base",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4cf1be8a009ea2949da3a30b4224ef4fbe93adb2a28fe2c7ca0e30f0c78aa145
+size 498649702

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "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
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}