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README.md

@@ -1,84 +1,447 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- sentence-transformers/all-MiniLM-L6-v2
-tags:
-- data science
-- Machine Learning
-metrics:
-- spearmanr
-- pearsonr
-library_name: sentence-transformers
----
-# Fine-tuned all-MiniLM-L6-v2 for Data Science
-
-## Model Details
-- **Base model:** [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)  
-- **Architecture:** Transformer-based sentence embedding model  
-- **License:** Apache 2.0  
-- **Fine-tuned by:** Digital Asocial  
-- **Languages:** English  
-
-This model is a fine-tuned version of all-MiniLM-L6-v2, optimized for semantic embeddings in the domain of Data Science and Machine Learning.
-
----
-
-## Intended Uses
-- Semantic search in Data Science and Machine Learning literature  
-- Embedding generation for academic projects, research, and applied ML tasks  
-- Document clustering, similarity search, and retrieval-augmented generation (RAG) pipelines  
-
-⚠️ **Not intended for:** verbatim reproduction of copyrighted texts.
-
----
-
-## Training Data
-The model was fine-tuned using 17 reference books in Data Science and Machine Learning, including:
-
-1. Aßenmacher, Matthias. *Multimodal Deep Learning*. Self-published, 2023.  
-2. Bertsekas, Dimitri P. *A Course in Reinforcement Learning*. Arizona State University.  
-3. Boykis, Vicki. *What are Embeddings*. Self-published, 2023.  
-4. Bruce, Peter, and Andrew Bruce. *Practical Statistics for Data Scientists: 50 Essential Concepts*. O’Reilly Media, 2017.  
-5. Daumé III, Hal. *A Course in Machine Learning*. Self-published.  
-6. Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. *Mathematics for Machine Learning*. Cambridge University Press, 2020.  
-7. Devlin, Hannah, Guo Kunin, Xiang Tian. *Seeing Theory*. Self-published.  
-8. Gutmann, Michael U. *Pen & Paper: Exercises in Machine Learning*. Self-published.  
-9. Jung, Alexander. *Machine Learning: The Basics*. Springer, 2022.  
-10. Langr, Jakub, and Vladimir Bok. *Deep Learning with Generative Adversarial Networks*. Manning Publications, 2019.  
-11. MacKay, David J.C. *Information Theory, Inference, and Learning Algorithms*. Cambridge University Press, 2003.  
-12. Montgomery, Douglas C., Cheryl L. Jennings, and Murat Kulahci. *Introduction to Time Series Analysis and Forecasting*. 2nd Edition, Wiley, 2015.  
-13. Nilsson, Nils J. *Introduction to Machine Learning: An Early Draft of a Proposed Textbook*. Stanford University, 1996.  
-14. Prince, Simon J.D. *Understanding Deep Learning*. Draft Edition, 2024.  
-15. Shashua, Amnon. *Introduction to Machine Learning*. The Hebrew University of Jerusalem, 2008.  
-16. Sutton, Richard S., and Andrew G. Barto. *Reinforcement Learning: An Introduction*. 2nd Edition, MIT Press, 2018.  
-17. Alpaydin, Ethem. *Introduction to Machine Learning*. 3rd Edition, MIT Press, 2014.  
-
-⚠️ **Note:** Due to copyright restrictions, the full text of these books is **not included** in this repository. Only the fine-tuned model weights are shared.
-
----
-
-## Limitations
-- The model may reflect biases present in the training material.  
-- It should not be used to regenerate or distribute copyrighted content.  
-- Performance is optimized for Data Science texts; results may vary in other domains.  
-
----
-
-## License
-- Base model: Apache 2.0 (Microsoft)  
-- Fine-tuned model: distributed under Apache 2.0  
-- Training data: proprietary or mixed-license sources. Not redistributed.  
-
----
-
-## Citation
-If you use this model, please cite:
-@misc{digitalasocial2025rag,
-  author = {digitalasocial},
-  title = {Fine-tuned all-mpnet-base-v2 for Data Science RAG},
-  year = {2025},
-  howpublished = {\url{https://huggingface.co/your-hf-username/all-mpnet-base-v2-ds-rag}}
-}
-
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:167112
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Our final beliefs about θ combine both the relevant information
+    we had a priori and the knowledge we gained a posteriori by observing data.
+  sentences:
+  - To get an understanding of what the Bayesian machinery looks like in action, let
+    us return to our coin toss example.
+  - In each case, the model returns a vector of size N that contains the probabilities
+    of the N categories.
+  - For the moment, in fact, let us consider draws and losses to be equally bad for
+    us.
+- source_sentence: 57, 58, 62, 63, 77, 87, 91, 104, 137, 159, 261  logistic regression
+    Logistic regression aims at learning a linear hypothesis map to predict a binary
+    label based on numeric features of a data point.
+  sentences:
+  - Transfer learning is enabled by constructing regularization terms for a learning
+    task by using the result of a previous leaning task.
+  - Propose a tree induction algorithm with backtracking.
+  - The quality of a linear hypothesis map (classifier) is measured using its average
+    logistic loss on some labeled datapoints (the training set).
+- source_sentence: The fourth column in table  2 .9 shows the Shannon information
+    content of the 27 possible outcomes when a random character is picked from an
+    English document.
+  sentences:
+  - 'If you are ready, let''s load up ye olde, trusty machine learning libraries and
+    get cracking: import tensorflow as tf import keras as K
+
+
+    In the code, progressive smoothing in may look something like the following listing.'
+  - The test set, which is constructed in step 3 of Algorithm 7, consists of data
+    points that are neither contained in the training set (6.9) nor the validation
+    set (6.11) used for training and validating the candidate models H (1) , .
+  - 'The entropy of an ensemble X is defined to be the average Shannon information
+    content of an outcome:
+
+
+    with the convention for P (x) = 0 that 0 × log 1/0 ≡ 0, since lim θ→0 + θ log
+    1/θ = 0.'
+- source_sentence: where β is known as the Lipschitz constant and determines the maximum
+    gradient of the function (i.e., how fast the function can change) with respect
+    to the distance metric.
+  sentences:
+  - First, let's consider the evidence that depth is required.
+  - 'By hand side of equation 7.17, we have:
+
+
+    • The derivative ∂ℓ i /∂f 3 of the loss ℓ i with respect to the network output
+    f 3 will depend on the loss function but usually has a simple form.'
+  - If the Lipschitz constant is less than one, the function is a contraction mapping,
+    and we can use Banach's theorem to find the inverse for any point (see figure  16
+    .9).
+- source_sentence: Can you explain why this might have been so?
+  sentences:
+  - See  http://www.cs.utexas.edu/~ikarpov/Classes/RL/RandomWalk/  for an attempt
+    at a thorough answer by Igor Karpov.
+  - We describe methods to choose the initial weights Ω k and biases β k so that training
+    is stable.
+  - (3.42) is used widely in practice.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: val
+      type: val
+    metrics:
+    - type: pearson_cosine
+      value: .nan
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: .nan
+      name: Spearman Cosine
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 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, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 384, '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})
+  (2): Normalize()
+)
+```
+
+## 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 = [
+    'Can you explain why this might have been so?',
+    'See  http://www.cs.utexas.edu/~ikarpov/Classes/RL/RandomWalk/  for an attempt at a thorough answer by Igor Karpov.',
+    'We describe methods to choose the initial weights Ω k and biases β k so that training is stable.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.3878, 0.2539],
+#         [0.3878, 1.0000, 0.2993],
+#         [0.2539, 0.2993, 1.0000]])
+```
+
+<!--
+### 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: `val`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value   |
+|:--------------------|:--------|
+| pearson_cosine      | nan     |
+| **spearman_cosine** | **nan** |
+
+<!--
+## 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: 167,112 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: 7 tokens</li><li>mean: 31.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 33.66 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                             | sentence_1                                                                                                                                                     |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>On the other hand, squared loss thinks it's just as bad to predict +3 on a positive example as it is to predict -1 on a positive example.</code> | <code>If you replace to zero/one loss with a surrogate loss, you obtain the following objective:<br><br>The question is: what should R(w, b) look like?</code> |
+  | <code>Why does the update step set the 'mean' to the mean of the assigned points?</code>                                                               | <code>Where did the distance d come from?</code>                                                                                                               |
+  | <code>We start with a training set {x i } of real 1D examples.</code>                                                                                  | <code>A different batch of ten of these examples {x i } 10 i=1 is shown in each panel (cyan arrows).</code>                                                    |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 15
+- `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
+- `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`: 15
+- `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
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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
+- `hub_revision`: None
+- `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`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | val_spearman_cosine |
+|:------:|:-----:|:-------------:|:-------------------:|
+| 0.0479 | 500   | 1.5544        | -                   |
+| 0.0957 | 1000  | 1.5054        | -                   |
+| 0.1436 | 1500  | 1.4649        | -                   |
+| 0.1915 | 2000  | 1.4132        | -                   |
+| 0.2393 | 2500  | 1.3963        | -                   |
+| 0.2872 | 3000  | 1.3248        | -                   |
+| 0.3351 | 3500  | 1.3158        | -                   |
+| 0.3830 | 4000  | 1.2871        | -                   |
+| 0.4308 | 4500  | 1.2491        | -                   |
+| 0.4787 | 5000  | 1.1982        | -                   |
+| 0.5266 | 5500  | 1.1764        | -                   |
+| 0.5744 | 6000  | 1.1958        | -                   |
+| 0.6223 | 6500  | 1.1286        | -                   |
+| 0.6702 | 7000  | 1.1255        | -                   |
+| 0.7180 | 7500  | 1.0662        | -                   |
+| 0.7659 | 8000  | 1.0896        | -                   |
+| 0.8138 | 8500  | 1.0574        | -                   |
+| 0.8617 | 9000  | 1.0571        | -                   |
+| 0.9095 | 9500  | 1.0138        | -                   |
+| 0.9574 | 10000 | 0.9956        | -                   |
+| 1.0    | 10445 | -             | nan                 |
+
+
+### Framework Versions
+- Python: 3.11.7
+- Sentence Transformers: 5.1.1
+- Transformers: 4.57.0
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.10.1
+- Datasets: 4.2.0
+- Tokenizers: 0.22.1
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@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}
+}
+```
+
+<!--
+## 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 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.57.0",
+    "pytorch": "2.5.1+cu121"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:752e9b710420fc2732918c4e295f8e68d561ccd673602d7ec7f16b0ba0fe06e7
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "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": false,
+    "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 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+  "extra_special_tokens": {},
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+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
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+  "padding_side": "right",
+  "sep_token": "[SEP]",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}