Add new SentenceTransformer 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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+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:800
+- loss:TripletLoss
+base_model: sentence-transformers/all-mpnet-base-v2
+datasets: []
+widget:
+- source_sentence: What is the advice given about the use of color in dataviz?
+  sentences:
+  - Don't use color if they communicate nothing.
+  - Four problems with Pie Charts are detailed in a guide by iCharts.net.
+  - Always use bright colors for highlighting important data.
+- source_sentence: What is the effect of a large sample size on the use of jitter
+    in a boxplot?
+  sentences:
+  - A large sample size will enhance the use of jitter in a boxplot.
+  - If you have a large sample size, using jitter is not an option anymore since dots
+    will overlap, making the figure uninterpretable.
+  - It is a good practice to use small multiples.
+- source_sentence: What is a suitable usage of pie charts in data visualization?
+  sentences:
+  - If you have a single series to display and all quantitative variables have the
+    same scale, then use a barplot or a lollipop plot, ranking the variables.
+  - Pie charts rapidly show parts to a whole better than any other plot. They are
+    most effective when used to compare parts to the whole.
+  - Pie charts are a flawed chart which can sometimes be justified if the differences
+    between groups are large.
+- source_sentence: Where can a note on long labels be found?
+  sentences:
+  - https://www.data-to-viz.com/caveat/hard_label.html
+  - A pie chart can tell a story very well; that all the data points as a percentage
+    of the whole are very similar.
+  - https://twitter.com/r_graph_gallery?lang=en
+- source_sentence: What is the reason pie plots can work as well as bar plots in some
+    scenarios?
+  sentences:
+  - Pie plots can work well for comparing portions a whole or portions one another,
+    especially when dealing with a single digit count of items.
+  - https://www.r-graph-gallery.com/line-plot/ and https://python-graph-gallery.com/line-chart/
+  - Thanks for your comment Tom, I do agree with you.
+pipeline_tag: sentence-similarity
+---
+
+# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (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("edubm/vis-sim-triplets-mpnet")
+# Run inference
+sentences = [
+    'What is the reason pie plots can work as well as bar plots in some scenarios?',
+    'Pie plots can work well for comparing portions a whole or portions one another, especially when dealing with a single digit count of items.',
+    'Thanks for your comment Tom, I do agree with you.',
+]
+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
+
+#### Unnamed Dataset
+
+
+* Size: 800 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 15.26 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 23.25 tokens</li><li>max: 306 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.38 tokens</li><li>max: 57 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                 | positive                                                                                                                                              | negative                                                                                                     |
+  |:-------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|
+  | <code>Did you ever figure out a solution to the error message problem when using your own data?</code> | <code>Yes, a solution was found. You have to add ' group = name ' inside the ' ggplot(aes())' like ggplot(aes(x=year, y=n,group=name)).</code>        | <code>I recommend sorting by some feature of the data, instead of in alphabetical order of the names.</code> |
+  | <code>Why should you consider reordering your data when building a chart?</code>                       | <code>Reordering your data can help in better visualization. Sometimes the order of groups must be set by their features and not their values.</code> | <code>You should reorder your data to clean it.</code>                                                       |
+  | <code>What is represented on the X-axis of the chart?</code>                                           | <code>The price ranges cut in several 10 euro bins.</code>                                                                                            | <code>The number of apartments per bin.</code>                                                               |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 200 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 14.99 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 22.38 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.08 tokens</li><li>max: 58 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                        | positive                                                                                                                                                                         | negative                                                                                             |
+  |:--------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|
+  | <code>What can be inferred about group C and B from the jittered boxplot?</code>                              | <code>Group C has a small sample size compared to the other groups. Group B seems to have a bimodal distribution with dots distributed in 2 groups: around y=18 and y=13.</code> | <code>Group C has the largest sample size and Group B has dots evenly distributed.</code>            |
+  | <code>What can cause a reduction in computing time and help avoid overplotting when dealing with data?</code> | <code>Plotting only a fraction of your data can cause a reduction in computing time and help avoid overplotting.</code>                                                          | <code>Plotting all of your data is the best method to reduce computing time.</code>                  |
+  | <code>How can area charts be used for data visualization?</code>                                              | <code>Area charts can be used to give a more general overview of the dataset, especially when used in combination with small multiples.</code>                                   | <code>Area charts make it obvious to spot a particular group in a crowded data visualization.</code> |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: 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
+- `learning_rate`: 5e-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`: 1
+- `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
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | Training Loss | loss   |
+|:-----:|:----:|:-------------:|:------:|
+| 0.02  | 1    | 4.8436        | 4.8922 |
+| 0.04  | 2    | 4.9583        | 4.8904 |
+| 0.06  | 3    | 4.8262        | 4.8862 |
+| 0.08  | 4    | 4.8961        | 4.8820 |
+| 0.1   | 5    | 4.9879        | 4.8754 |
+| 0.12  | 6    | 4.8599        | 4.8680 |
+| 0.14  | 7    | 4.9098        | 4.8586 |
+| 0.16  | 8    | 4.8802        | 4.8496 |
+| 0.18  | 9    | 4.8797        | 4.8392 |
+| 0.2   | 10   | 4.8691        | 4.8307 |
+| 0.22  | 11   | 4.9213        | 4.8224 |
+| 0.24  | 12   | 4.88          | 4.8145 |
+| 0.26  | 13   | 4.9131        | 4.8071 |
+| 0.28  | 14   | 4.7596        | 4.8004 |
+| 0.3   | 15   | 4.8388        | 4.7962 |
+| 0.32  | 16   | 4.8434        | 4.7945 |
+| 0.34  | 17   | 4.8726        | 4.7939 |
+| 0.36  | 18   | 4.8049        | 4.7943 |
+| 0.38  | 19   | 4.8225        | 4.7932 |
+| 0.4   | 20   | 4.7631        | 4.7900 |
+| 0.42  | 21   | 4.7841        | 4.7847 |
+| 0.44  | 22   | 4.8077        | 4.7759 |
+| 0.46  | 23   | 4.7731        | 4.7678 |
+| 0.48  | 24   | 4.7623        | 4.7589 |
+| 0.5   | 25   | 4.8572        | 4.7502 |
+| 0.52  | 26   | 4.843         | 4.7392 |
+| 0.54  | 27   | 4.6826        | 4.7292 |
+| 0.56  | 28   | 4.7584        | 4.7180 |
+| 0.58  | 29   | 4.7281        | 4.7078 |
+| 0.6   | 30   | 4.7491        | 4.6982 |
+| 0.62  | 31   | 4.7501        | 4.6897 |
+| 0.64  | 32   | 4.6219        | 4.6826 |
+| 0.66  | 33   | 4.7323        | 4.6768 |
+| 0.68  | 34   | 4.5499        | 4.6702 |
+| 0.7   | 35   | 4.7682        | 4.6648 |
+| 0.72  | 36   | 4.6483        | 4.6589 |
+| 0.74  | 37   | 4.6675        | 4.6589 |
+| 0.76  | 38   | 4.7389        | 4.6527 |
+| 0.78  | 39   | 4.7721        | 4.6465 |
+| 0.8   | 40   | 4.6043        | 4.6418 |
+| 0.82  | 41   | 4.7894        | 4.6375 |
+| 0.84  | 42   | 4.6134        | 4.6341 |
+| 0.86  | 43   | 4.6664        | 4.6307 |
+| 0.88  | 44   | 4.5249        | 4.6264 |
+| 0.9   | 45   | 4.7045        | 4.6227 |
+| 0.92  | 46   | 4.7231        | 4.6198 |
+| 0.94  | 47   | 4.7011        | 4.6176 |
+| 0.96  | 48   | 4.5876        | 4.6159 |
+| 0.98  | 49   | 4.7567        | 4.6146 |
+| 1.0   | 50   | 4.6706        | 4.6138 |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification}, 
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e75b1b19a25cd119a489a2533939877edee040900b401701ddda3d863261419
+size 437967672

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": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "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": "</s>",
+    "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 +1,72 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}