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README.md

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+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:1485
+- loss:BinaryCrossEntropyLoss
+base_model: cross-encoder/ms-marco-MiniLM-L12-v2
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- accuracy
+- accuracy_threshold
+- f1
+- f1_threshold
+- precision
+- recall
+- average_precision
+model-index:
+- name: CrossEncoder based on cross-encoder/ms-marco-MiniLM-L12-v2
+  results:
+  - task:
+      type: cross-encoder-classification
+      name: Cross Encoder Classification
+    dataset:
+      name: compliance eval
+      type: compliance-eval
+    metrics:
+    - type: accuracy
+      value: 0.9636363636363636
+      name: Accuracy
+    - type: accuracy_threshold
+      value: -1.7519245147705078
+      name: Accuracy Threshold
+    - type: f1
+      value: 0.9662921348314608
+      name: F1
+    - type: f1_threshold
+      value: -2.8691844940185547
+      name: F1 Threshold
+    - type: precision
+      value: 0.9555555555555556
+      name: Precision
+    - type: recall
+      value: 0.9772727272727273
+      name: Recall
+    - type: average_precision
+      value: 0.9939968601076801
+      name: Average Precision
+---
+
+# CrossEncoder based on cross-encoder/ms-marco-MiniLM-L12-v2
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) <!-- at revision 7b0235231ca2674cb8ca8f022859a6eba2b1c968 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## 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 CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("cross_encoder_model_id")
+# Get scores for pairs of texts
+pairs = [
+    ['the system must identify any risk profile that has expired and is currently marked as overdue to ensure ongoing suitability compliance.', "so, like, your portfolio risk profile is out of date, and i've got a flag here saying it needs renewal before we can do any new trades."],
+    ['to identify risk misalignment trades, the system must flag a risk mismatch whenever the product risk rating exceeds the client risk profile.', "so, it's a solid choice, but i gotta mention, there's a bit of a risk mismatch between the fund's rating and your own suitability score, so it's a bit of a hurdle."],
+    ['the system identifies an execution only wrapper when the order initiation confirms that this trade is performed on an execution only basis with no advice given.', "so... uh... let's just do it, but it's execution only, you know? no advice was provided, so you're on your own with the strategy on this one, i'm so rushed."],
+    ['the system must identify any risk profile that has expired and is currently marked as overdue to ensure ongoing suitability compliance.', "hey, um, checking the dashboard here and it says your prp is overdue, you know, we haven't updated it in a bit and it's flagged."],
+    ['to identify risk misalignment trades, the system must flag a risk mismatch whenever the product risk rating exceeds the client risk profile.', "don't worry about the specifics right now the main thing is getting the allocation because it's oversubscribed so can i confirm the trade"],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'the system must identify any risk profile that has expired and is currently marked as overdue to ensure ongoing suitability compliance.',
+    [
+        "so, like, your portfolio risk profile is out of date, and i've got a flag here saying it needs renewal before we can do any new trades.",
+        "so, it's a solid choice, but i gotta mention, there's a bit of a risk mismatch between the fund's rating and your own suitability score, so it's a bit of a hurdle.",
+        "so... uh... let's just do it, but it's execution only, you know? no advice was provided, so you're on your own with the strategy on this one, i'm so rushed.",
+        "hey, um, checking the dashboard here and it says your prp is overdue, you know, we haven't updated it in a bit and it's flagged.",
+        "don't worry about the specifics right now the main thing is getting the allocation because it's oversubscribed so can i confirm the trade",
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### 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
+
+#### Cross Encoder Classification
+
+* Dataset: `compliance-eval`
+* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
+
+| Metric                | Value     |
+|:----------------------|:----------|
+| accuracy              | 0.9636    |
+| accuracy_threshold    | -1.7519   |
+| f1                    | 0.9663    |
+| f1_threshold          | -2.8692   |
+| precision             | 0.9556    |
+| recall                | 0.9773    |
+| **average_precision** | **0.994** |
+
+<!--
+## 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: 1,485 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                                         | sentence2                                                                                       | label                                                          |
+  |:--------|:--------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                                            | string                                                                                          | float                                                          |
+  | details | <ul><li>min: 135 characters</li><li>mean: 302.95 characters</li><li>max: 725 characters</li></ul> | <ul><li>min: 97 characters</li><li>mean: 179.3 characters</li><li>max: 463 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | sentence2                                                                                                                                                                                                               | label            |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>the rm must use the instrument_code to identify the soft lock disclosure and inform the client that 'this fund has a soft lock-up duration of xx months. you will be subjected to an early redemption charge of x% by the fund house if you were to redeem the fund within the soft lock-up period.' and, if applicable, that 'the fund is currently still within the soft lock-up period. should you wish to proceed with the redemption, you will incur an early redemption charge of x% by the fund house.'</code> | <code>there's a bit of a soft lock on this one, you know, if you take the money out too soon there's a small charge, but it's no big deal.</code>                                                                       | <code>0.0</code> |
+  | <code>the system identifies an execution only wrapper when the order initiation confirms that this trade is performed on an execution only basis with no advice given.</code>                                                                                                                                                                                                                                                                                                                                               | <code>i can't believe how expensive flights have become lately, it's just ridiculous. let's just go ahead with that stock buy, i'll put it through as we discussed earlier, it’s a simple execution for us.</code>      | <code>0.0</code> |
+  | <code>for a client initiated (ci) wrapper where the order initiation is 'client initiated', the bank must confirm that 'this trade is based on your initiated interest in underlying and product type' or 'this trade is based on your initiated interest in underlying or product type'.</code>                                                                                                                                                                                                                            | <code>exactly, i-i see what you mean, and since you're the one who initiated this conversation about the emerging markets fund, i'll just log that as your interest. did you ever get that classic car fixed up?</code> | <code>1.0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": null
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 165 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 165 samples:
+  |         | sentence1                                                                                         | sentence2                                                                                        | label                                                          |
+  |:--------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                                            | string                                                                                           | float                                                          |
+  | details | <ul><li>min: 135 characters</li><li>mean: 302.44 characters</li><li>max: 725 characters</li></ul> | <ul><li>min: 97 characters</li><li>mean: 178.02 characters</li><li>max: 631 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                     | sentence2                                                                                                                                                                        | label            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>the system must identify any risk profile that has expired and is currently marked as overdue to ensure ongoing suitability compliance.</code>                          | <code>so, like, your portfolio risk profile is out of date, and i've got a flag here saying it needs renewal before we can do any new trades.</code>                             | <code>1.0</code> |
+  | <code>to identify risk misalignment trades, the system must flag a risk mismatch whenever the product risk rating exceeds the client risk profile.</code>                     | <code>so, it's a solid choice, but i gotta mention, there's a bit of a risk mismatch between the fund's rating and your own suitability score, so it's a bit of a hurdle.</code> | <code>1.0</code> |
+  | <code>the system identifies an execution only wrapper when the order initiation confirms that this trade is performed on an execution only basis with no advice given.</code> | <code>so... uh... let's just do it, but it's execution only, you know? no advice was provided, so you're on your own with the strategy on this one, i'm so rushed.</code>        | <code>1.0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": null
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.1
+- `load_best_model_at_end`: True
+
+#### 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`: 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`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: True
+- `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`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch     | Step    | Training Loss | Validation Loss | compliance-eval_average_precision |
+|:---------:|:-------:|:-------------:|:---------------:|:---------------------------------:|
+| 0.1075    | 10      | 1.9119        | 1.1985          | 0.6783                            |
+| 0.2151    | 20      | 0.9675        | 1.0970          | 0.6914                            |
+| 0.3226    | 30      | 0.7458        | 0.4725          | 0.8480                            |
+| 0.4301    | 40      | 0.5308        | 0.4431          | 0.8849                            |
+| 0.5376    | 50      | 0.3888        | 0.4183          | 0.9097                            |
+| 0.6452    | 60      | 0.3477        | 0.3472          | 0.9325                            |
+| 0.7527    | 70      | 0.3082        | 0.3005          | 0.9524                            |
+| 0.8602    | 80      | 0.3364        | 0.2682          | 0.9647                            |
+| 0.9677    | 90      | 0.3069        | 0.2345          | 0.9804                            |
+| 1.0753    | 100     | 0.2636        | 0.1847          | 0.9886                            |
+| 1.1828    | 110     | 0.2577        | 0.1793          | 0.9847                            |
+| 1.2903    | 120     | 0.1793        | 0.1940          | 0.9826                            |
+| 1.3978    | 130     | 0.19          | 0.2333          | 0.9794                            |
+| 1.5054    | 140     | 0.1788        | 0.1615          | 0.9858                            |
+| 1.6129    | 150     | 0.1277        | 0.1576          | 0.9862                            |
+| 1.7204    | 160     | 0.1851        | 0.1399          | 0.9903                            |
+| 1.8280    | 170     | 0.1652        | 0.1056          | 0.9947                            |
+| 1.9355    | 180     | 0.085         | 0.1077          | 0.9949                            |
+| **2.043** | **190** | **0.1111**    | **0.0943**      | **0.9955**                        |
+| 2.1505    | 200     | 0.09          | 0.1137          | 0.9955                            |
+| 2.2581    | 210     | 0.1136        | 0.1222          | 0.9934                            |
+| 2.3656    | 220     | 0.0703        | 0.1155          | 0.9937                            |
+| 2.4731    | 230     | 0.0866        | 0.1147          | 0.9935                            |
+| 2.5806    | 240     | 0.1104        | 0.1089          | 0.9943                            |
+| 2.6882    | 250     | 0.1523        | 0.1141          | 0.9940                            |
+| 2.7957    | 260     | 0.1189        | 0.1297          | 0.9943                            |
+| 2.9032    | 270     | 0.0479        | 0.1365          | 0.9940                            |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.2.0
+- Transformers: 4.57.3
+- PyTorch: 2.9.0+cu126
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- 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 +1,35 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "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,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "5.2.0"
+  },
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77e20bbd161576eccc72a4009e86c4c3b2270a3bcb445253af8352ff9f145252
+size 133464836

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 +1,58 @@
+{
+  "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_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}