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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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

@@ -1,3 +1,460 @@
----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:36864
+- loss:MatryoshkaLoss
+- loss:CachedMultipleNegativesRankingLoss
+base_model: redis/langcache-embed-v1
+widget:
+- source_sentence: What are civil cases and what are some examples?
+  sentences:
+  - What are criminal cases and what are no examples?
+  - Civil cases involve disputes between individuals or organizations, typically seeking
+    monetary compensation or specific performance, and *do not* include criminal prosecutions
+    by the government.
+  - Criminal cases involve disputes between individuals or organizations, seeking
+    monetary damages or specific performance, while civil cases concern offenses against
+    the state punishable by imprisonment.
+  - What are some examples of civil cases?
+- source_sentence: How can you stop your palms from sweating?
+  sentences:
+  - How do I stop my palms from sweating a lot at random times?
+  - How can you *make* your palms sweat?
+  - How can you *cause* your palms to sweat?
+  - How can you start your palms from sweating?
+- source_sentence: What are the pros and cons of wind turbines?
+  sentences:
+  - What are the pros and cons of solar panels?
+  - What are the cons and pros of solar panels?
+  - What are pros and cons of wind turbines?
+  - Wind turbines have no advantages or disadvantages.
+- source_sentence: Will Obamacare be repealed now that trump won?
+  sentences:
+  - Despite Trump's victory, Obamacare remains largely intact and has not been fully
+    repealed.
+  - Despite Trump's repeated promises to repeal and replace the Affordable Care Act
+    (ACA), often called Obamacare, it remains the law of the land. Numerous attempts
+    to repeal or significantly alter the ACA failed during his presidency due to Congressional
+    opposition.
+  - Will Obamacare be repealed now that Biden won?
+  - Will Obamacare be repealed / shut down soon?
+- source_sentence: What are some examples of crimes understood as a moral turpitude?
+  sentences:
+  - What actions are *not* generally considered crimes involving moral turpitude?
+  - What are some examples of crimes understood as a legal aptitude?
+  - What are some examples of crimes understood as a legal turpitude?
+  - What are some examples of crimes of moral turpitude?
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on redis/langcache-embed-v1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [redis/langcache-embed-v1](https://huggingface.co/redis/langcache-embed-v1) on the triplet dataset. 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:** [redis/langcache-embed-v1](https://huggingface.co/redis/langcache-embed-v1) <!-- at revision 80fb95b5478a6b6d068faf4452faa2f5bc9f0dfa -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - triplet
+<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("waris-gill/langcache-embed-v2")
+# Run inference
+sentences = [
+    'What are some examples of crimes understood as a moral turpitude?',
+    'What are some examples of crimes of moral turpitude?',
+    'What are some examples of crimes understood as a legal aptitude?',
+]
+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
+
+#### triplet
+
+* Dataset: triplet
+* Size: 36,864 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, and <code>negative_3</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative_1                                                                         | negative_2                                                                         | negative_3                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                             | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.88 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.89 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.68 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.26 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.07 tokens</li><li>max: 108 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                       | positive                                                            | negative_1                                                                                                                                                                 | negative_2                                                                         | negative_3                                                                                                                                   |
+  |:---------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Is life really what I make of it?</code>                                               | <code>Life is what you make it?</code>                              | <code>Is life hardly what I take of it?</code>                                                                                                                             | <code>Life is not entirely what I make of it.</code>                               | <code>Is life not what I make of it?</code>                                                                                                  |
+  | <code>When you visit a website, can a person running the website see your IP address?</code> | <code>Does every website I visit knows my public ip address?</code> | <code>When you avoid a website, can a person hiding the website see your MAC address?</code>                                                                               | <code>When you send an email, can the recipient see your physical location?</code> | <code>When you visit a website, a person running the website cannot see your IP address.</code>                                              |
+  | <code>What are some cool features about iOS 10?</code>                                       | <code>What are the best new features of iOS 10?</code>              | <code>iOS 10 received criticism for its initial bugs and performance issues, and some users found the redesigned apps less intuitive compared to previous versions.</code> | <code>What are the drawbacks of using Android 14?</code>                           | <code>iOS 10 was widely criticized for its bugs, removal of beloved features, and generally being a downgrade from previous versions.</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "CachedMultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### triplet
+
+* Dataset: triplet
+* Size: 7,267 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, and <code>negative_3</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative_1                                                                         | negative_2                                                                        | negative_3                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                             | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.62 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.58 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.32 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.1 tokens</li><li>max: 174 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.26 tokens</li><li>max: 172 tokens</li></ul> |
+* Samples:
+  | anchor                                                                              | positive                                                       | negative_1                                                                                      | negative_2                                                                                                                                                                                      | negative_3                                                                          |
+  |:------------------------------------------------------------------------------------|:---------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | <code>How do I make friends in office?</code>                                       | <code>How can I make friends in office?</code>                 | <code>How do I lose friends in office?</code>                                                   | <code>How do I lose enemies in office?</code>                                                                                                                                                   | <code>I already have plenty of friends at work.</code>                              |
+  | <code>Is it good to do MBA after Engineering?</code>                                | <code>Is it necessary to do MBA after Engineering?</code>      | <code>Is learning to code essential for a successful marketing career?</code>                   | <code>Not necessarily; an MBA isn't *always* the best next step after engineering – practical experience or specialized master's degrees can be more valuable depending on career goals.</code> | <code>Is it bad to do MBA after Engineering?</code>                                 |
+  | <code>How I should fix my computer while it is showing no boot device found?</code> | <code>How do I fix the "Boot device not found" problem?</code> | <code>My computer is booting normally and does not have any issues with the boot device.</code> | <code>I should not fix my computer while it is showing no boot device found.</code>                                                                                                             | <code>When will I break my phone while it is showing full boot device found?</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "CachedMultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 2048
+- `per_device_eval_batch_size`: 1024
+- `learning_rate`: 1e-05
+- `num_train_epochs`: 1
+- `lr_scheduler_type`: constant
+- `warmup_steps`: 10
+- `gradient_checkpointing`: True
+- `torch_compile`: True
+- `torch_compile_backend`: inductor
+- `batch_sampler`: no_duplicates
+
+#### 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`: 2048
+- `per_device_eval_batch_size`: 1024
+- `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`: 1e-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`: constant
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 10
+- `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}
+- `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`: True
+- `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`: True
+- `torch_compile_backend`: inductor
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | triplet loss |
+|:------:|:----:|:-------------:|:------------:|
+| 0.0556 | 1    | 6.4636        | -            |
+| 0.1111 | 2    | 6.1076        | -            |
+| 0.1667 | 3    | 5.8323        | -            |
+| 0.2222 | 4    | 5.6861        | -            |
+| 0.2778 | 5    | 5.5694        | -            |
+| 0.3333 | 6    | 5.2121        | -            |
+| 0.3889 | 7    | 5.0695        | -            |
+| 0.4444 | 8    | 4.81          | -            |
+| 0.5    | 9    | 4.6698        | -            |
+| 0.5556 | 10   | 4.3546        | 1.2224       |
+| 0.6111 | 11   | 4.1922        | -            |
+| 0.6667 | 12   | 4.1434        | -            |
+| 0.7222 | 13   | 3.9918        | -            |
+| 0.7778 | 14   | 3.702         | -            |
+| 0.8333 | 15   | 3.6501        | -            |
+| 0.8889 | 16   | 3.6641        | -            |
+| 0.9444 | 17   | 3.3196        | -            |
+| 1.0    | 18   | 2.7108        | -            |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.1
+- 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## 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 +1,45 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b03b5944f321a492c7e17cb848d3ae5bb1baec29e44aa68696c77217603919ce
+size 596070136

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"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,952 @@
+{
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+}