Upload e5-base-john embedding model

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

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:2633
+- loss:CosineSimilarityLoss
+base_model: intfloat/e5-base-v2
+widget:
+- source_sentence: Many therefore of his disciples, when they had heard this, said,
+    This is an hard saying; who can hear it?
+  sentences:
+  - If ye keep my commandments, ye shall abide in my love; even as I have kept my
+    Father's commandments, and abide in his love.
+  - When Jesus knew in himself that his disciples murmured at it, he said unto them,
+    Doth this offend you?
+  - He said, I am the voice of one crying in the wilderness, Make straight the way
+    of the Lord, as said the prophet Esaias.
+- source_sentence: 'Jesus and Nicodemus | participants: jesus_905, nicodemus_2204'
+  sentences:
+  - 'And as Moses lifted up the serpent in the wilderness, even so must the Son of
+    man be lifted up:'
+  - Then when Mary was come where Jesus was, and saw him, she fell down at his feet,
+    saying unto him, Lord, if thou hadst been here, my brother had not died.
+  - They answered him, Jesus of Nazareth. Jesus saith unto them, I am he. And Judas
+    also, which betrayed him, stood with them.
+- source_sentence: 'For he whom God hath sent speaketh the words of God: for God giveth
+    not the Spirit by measure unto him.'
+  sentences:
+  - Then said Jesus unto the twelve, Will ye also go away?
+  - The Father loveth the Son, and hath given all things into his hand.
+  - 'Why askest thou me? ask them which heard me, what I have said unto them: behold,
+    they know what I said.'
+- source_sentence: 'Lazarus Raised form the Dead | participants: jesus_905, mary_1939,
+    lazarus_1812'
+  sentences:
+  - But he saith unto them, It is I; be not afraid.
+  - But some of them went their ways to the Pharisees, and told them what things Jesus
+    had done.
+  - Jesus answered and said unto them, Destroy this temple, and in three days I will
+    raise it up.
+- source_sentence: 'God:  (A.S. and Dutch God; Dan. Gud; Ger. Gott), the name of the
+    Divine Being. It is the rendering (1) of the Hebrew <i> ''El</i> , from a word
+    meaning to be strong; (2) of <i> ''Eloah_, plural _''Elohim</i> . The singular
+    form, <i> Eloah</i> , is used only in poetry. The plural form is more commonly
+    used in all parts of the Bible, The Hebrew word Jehovah (q.v.), the only other
+    word generally employed to denote the Supreme Being, is uniformly rendered in
+    the Authorized Version by "LORD," printed in small capitals. The existence of
+    God is taken for granted in the Bible. There is nowhere any argument to prove
+    it. He who disbelieves this truth is spoken of as one devoid of understanding
+    (  Psalms 14:1  ).    The arguments generally adduced by theologians in proof
+    of the being of God are:   <li> The a priori argument, which is the testimony
+    afforded by reason.    <li> The a posteriori argument, by which we proceed logically
+    from the facts of experience to causes. These arguments are,    (a) The cosmological,
+    by which it is proved that there must be a First Cause of all things, for every
+    effect must have a cause.   (b) The teleological, or the argument from design.
+    We see everywhere the operations of an intelligent Cause in nature.   (c) The
+    moral argument, called also the anthropological argument, based on the moral consciousness
+    and the history of mankind, which exhibits a moral order and purpose which can
+    only be explained on the supposition of the existence of God. Conscience and human
+    history testify that "verily there is a God that judgeth in the earth."   The
+    attributes of God are set forth in order by Moses in   Exodus 34:6   Exodus 34:7  .
+    (see also   Deuteronomy 6:4  ;   10:17  ;   Numbers 16:22  ;   Exodus 15:11  ;   33:19  ;   Isaiah
+    44:6  ;   Habakkuk 3:6  ;   Psalms 102:26  ;   Job 34:12  .) They are also systematically
+    classified in   Revelation 5:12   and   7:12  .    God''s attributes are spoken
+    of by some as absolute, i.e., such as belong to his essence as Jehovah, Jah, etc.;
+    and relative, i.e., such as are ascribed to him with relation to his creatures.
+    Others distinguish them into communicable, i.e., those which can be imparted in
+    degree to his creatures: goodness, holiness, wisdom, etc.; and incommunicable,
+    which cannot be so imparted: independence, immutability, immensity, and eternity.
+    They are by some also divided into natural attributes, eternity, immensity, etc.;
+    and moral, holiness, goodness, etc.'
+  sentences:
+  - As he spake these words, many believed on him.
+  - 'Jesus said unto them, If God were your Father, ye would love me: for I proceeded
+    forth and came from God; neither came I of myself, but he sent me.'
+  - 'Jesus answered them, I told you, and ye believed not: the works that I do in
+    my Father''s name, they bear witness of me.'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on intfloat/e5-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). 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:** [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) <!-- at revision f52bf8ec8c7124536f0efb74aca902b2995e5bcd -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 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})
+  (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 = [
+    'God:  (A.S. and Dutch God; Dan. Gud; Ger. Gott), the name of the Divine Being. It is the rendering (1) of the Hebrew <i> \'El</i> , from a word meaning to be strong; (2) of <i> \'Eloah_, plural _\'Elohim</i> . The singular form, <i> Eloah</i> , is used only in poetry. The plural form is more commonly used in all parts of the Bible, The Hebrew word Jehovah (q.v.), the only other word generally employed to denote the Supreme Being, is uniformly rendered in the Authorized Version by "LORD," printed in small capitals. The existence of God is taken for granted in the Bible. There is nowhere any argument to prove it. He who disbelieves this truth is spoken of as one devoid of understanding (  Psalms 14:1  ).    The arguments generally adduced by theologians in proof of the being of God are:   <li> The a priori argument, which is the testimony afforded by reason.    <li> The a posteriori argument, by which we proceed logically from the facts of experience to causes. These arguments are,    (a) The cosmological, by which it is proved that there must be a First Cause of all things, for every effect must have a cause.   (b) The teleological, or the argument from design. We see everywhere the operations of an intelligent Cause in nature.   (c) The moral argument, called also the anthropological argument, based on the moral consciousness and the history of mankind, which exhibits a moral order and purpose which can only be explained on the supposition of the existence of God. Conscience and human history testify that "verily there is a God that judgeth in the earth."   The attributes of God are set forth in order by Moses in   Exodus 34:6   Exodus 34:7  . (see also   Deuteronomy 6:4  ;   10:17  ;   Numbers 16:22  ;   Exodus 15:11  ;   33:19  ;   Isaiah 44:6  ;   Habakkuk 3:6  ;   Psalms 102:26  ;   Job 34:12  .) They are also systematically classified in   Revelation 5:12   and   7:12  .    God\'s attributes are spoken of by some as absolute, i.e., such as belong to his essence as Jehovah, Jah, etc.; and relative, i.e., such as are ascribed to him with relation to his creatures. Others distinguish them into communicable, i.e., those which can be imparted in degree to his creatures: goodness, holiness, wisdom, etc.; and incommunicable, which cannot be so imparted: independence, immutability, immensity, and eternity. They are by some also divided into natural attributes, eternity, immensity, etc.; and moral, holiness, goodness, etc.',
+    'Jesus said unto them, If God were your Father, ye would love me: for I proceeded forth and came from God; neither came I of myself, but he sent me.',
+    'As he spake these words, many believed on him.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.7557, 0.7462],
+#         [0.7557, 1.0000, 0.7852],
+#         [0.7462, 0.7852, 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.*
+-->
+
+<!--
+## 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: 2,633 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: 3 tokens</li><li>mean: 81.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 30.06 tokens</li><li>max: 73 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>God:  (A.S. and Dutch God; Dan. Gud; Ger. Gott), the name of the Divine Being. It is the rendering (1) of the Hebrew <i> 'El</i> , from a word meaning to be strong; (2) of <i> 'Eloah_, plural _'Elohim</i> . The singular form, <i> Eloah</i> , is used only in poetry. The plural form is more commonly used in all parts of the Bible, The Hebrew word Jehovah (q.v.), the only other word generally employed to denote the Supreme Being, is uniformly rendered in the Authorized Version by "LORD," printed in small capitals. The existence of God is taken for granted in the Bible. There is nowhere any argument to prove it. He who disbelieves this truth is spoken of as one devoid of understanding (  Psalms 14:1  ).    The arguments generally adduced by theologians in proof of the being of God are:   <li> The a priori argument, which is the testimony afforded by reason.    <li> The a posteriori argument, by which we proceed logically from the facts of experience to causes. These arguments are,    (a) T...</code> | <code>For as the Father hath life in himself; so hath he given to the Son to have life in himself;</code>                              | <code>1.0</code> |
+  | <code>Bread of Life Sermon \| participants: jesus_905, peter_2745</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | <code>Jesus therefore answered and said unto them, Murmur not among yourselves.</code>                                                 | <code>1.0</code> |
+  | <code>Verily, verily, I say unto thee, When thou wast young, thou girdest thyself, and walkedst whither thou wouldest: but when thou shalt be old, thou shalt stretch forth thy hands, and another shall gird thee, and carry thee whither thou wouldest not.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>This spake he, signifying by what death he should glorify God. And when he had spoken this, he saith unto him, Follow me.</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`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `max_steps`: 5
+- `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`: 1
+- `max_steps`: 5
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `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_fused
+- `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>
+
+### Framework Versions
+- Python: 3.13.11
+- Sentence Transformers: 5.2.0
+- Transformers: 4.57.6
+- PyTorch: 2.10.0+cpu
+- Accelerate: 1.12.0
+- Datasets: 4.5.0
+- Tokenizers: 0.22.2
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

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": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.6",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.0",
+    "transformers": "4.57.6",
+    "pytorch": "2.10.0+cpu"
+  },
+  "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 sha256:0c5c6fe3d2c0a353c6994006c4ef77e6723cba4120e2bb49b58acd34b3614af0
+size 437951328

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 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}