initial commit

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -1,3 +1,391 @@
----
-license: apache-2.0
----
+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:704
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+datasets: []
+widget:
+- source_sentence: Apa statistik peserta MSIB Batch 4 dan 5?
+  sentences:
+  - Ijazah dicetak berdasarkan data preview yang dipermanen oleh calon wisudawan.
+    Ijazah dicetak hanya sekali saja, bila ada kekeliruan pengisian data pada point
+    3 maka akan diterbitkan surat keterangan
+  - 'MSIB Batch 4: Total Mahasiswa diterima: 664 mahasiswa; Program Magang: 228 mahasiswa;
+    Program Studi Independen: 436 mahasiswa; Jumlah Mitra Industri: 108 Mitra
+
+    MSIB Batch 5: Total Mahasiswa diterima: 488 mahasiswa; Program Magang: 257 mahasiswa;
+    Program Studi Independen: 231 mahasiswa; Jumlah Mitra Industri: 121 Mitra'
+  - Bentuk kegiatan studi/ proyek independen bisa berupa lomba - lomba kemahasiswaan
+    atau proyek - proyek untuk memecahkan persoalan di ITS, di masyarakat atau industri.
+- source_sentence: Apa deskripsi penelitian P12 tentang Numerical modeling and Experiments
+    of an atmospheric pressure plasma jet operated in air with shielding gas?
+  sentences:
+  - 'Deskripsi penelitian: Developing a two-dimensional axisymmetric plasma fluid
+    model integrated with a gas flow model to predict the dynamic behavior of a helium
+    atmospheric pressure plasma jet.'
+  - 'Sekilas Kerja Praktik
+
+    Jumlah kredit Mata Kuliah KP: 2 SKS'
+  - Nama pengarang, tahun publikasi, dan judul artikel/paper sama dengan penulisan
+    artikel dari jurnal. Judul buku dicetak miring/italic. Nomor volume dari buku
+    (jika ada). Edisi penerbitan. Nama editor didahului dengan ed. atau eds. bila
+    lebih dari satu editor. Nama penyelenggara seminar/conference. Kota tempat penyelenggaraan.
+    Nomor halaman dari artikel/paper tersebut di dalam prosiding.
+- source_sentence: Bagaimana format penulisan referensi proyek mahasiswa?
+  sentences:
+  - Gedung KPA, Plaza dr.Angka, Lantai 1, Kampus ITS Sukolilo Surabaya.
+  - Nama pengarang dan tahun publikasi sama dengan penulisan artikel dari jurnal.
+    Judul proyek dicetak miring/italic. Jenis proyek. Nama perguruan tinggi. Kota
+    tempat penyelenggaraan.
+  - Nama pengarang dan tahun publikasi sama dengan penulisan artikel dari jurnal.
+    Judul standar teknis dicetak miring/italic. Nama penerbit. Kota tempat diterbitkan.
+- source_sentence: MyITS saya bermasalah, bisa lapor kemana?
+  sentences:
+  - Mahasiswa yang ingin mendaftar sidang proposal tesis, harus melengkapi berkas
+    persyaratan pendaftaransidang proposal tesis, meliputi draft proposal tesis dan
+    lembar persetujuan pembimbing.
+  - 'B. Bagi Dosen Wali
+
+    2. Jika dinilai sudah sesuai, pengajuan perencanaan Kegiatan SKEM/ Program MB-KM
+    oleh mahasiswa, dosen wali dapat melakukan persetujuan FRS.'
+  - Silakan ajukan tiket ke DPTSI di https://servicedesk.its.ac.id/.
+- source_sentence: Apa saja panduan umum pelaksanaan Program Magang di ITS?
+  sentences:
+  - 'Mahasiswa dalam melaksanakan magang harus memenuhi ketentuan berikut: 1. Pelaksanaan
+    Program Magang memiliki durasi minimal 1 bulan dan maksimal 6 bulan. 2. Selama
+    Program Magang berlangsung, mahasiswa tidak harus mengajukan cuti. 3. Mahasiswa
+    secara penuh waktu bekerja di lapangan sesuai kesepakatan. 4. Mahasiswa bisa mendapatkan
+    izin untuk kegiatan akademik tertentu dengan kesepakatan pihak Mitra Magang. 5.
+    Mahasiswa dapat mengajukan konversi mata kuliah dengan CPMK yang selaras. 6. Diperlukan
+    pembimbing internal dari Dosen Departemen dan pembimbing lapangan dari Mitra Magang.
+    7. Sebelum Program Magang, wajib menandatangani perjanjian kerjasama dan nota
+    kesepahaman.'
+  - Pelaksanaan transfer kegiatan MB - KM menjadi sks mata kuliah, program studi atau
+    direktorat membentuk tim pelaksana transfer kredit.
+  - '1. Mahasiswa mengurus surat rekomendasi departemen untuk pengajuan magang ke
+    Mitra
+
+    2. Mahasiswa mengajukan permohonan magang ke Mitra
+
+    3. Mitra melakukan seleksi magang
+
+    4. Mahasiswa menerima hasil seleksi magang dari Mitra
+
+    5. Apabila tidak diterima, maka mahasiswa harus mengulang sejak langkah awal
+
+    6. Apabila diterima, Mahasiswa melaporkan ke Departemen
+
+    7. Mahasiswa/Departemen melakukan koordinasi dengan PK2 untuk pengurusan PKS dengan
+    menyertakan Proposal Magang, Surat Rekomendasi Departemen dan Surat Penerimaan
+    Magang
+
+    8. Proses pengurusan PKS (Dapat dilakukan bersamaan dengan pelaksanaan Magang)'
+pipeline_tag: sentence-similarity
+---
+
+# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 384 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': 128, 'do_lower_case': False}) with Transformer model: 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})
+)
+```
+
+## 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 = [
+    'Apa saja panduan umum pelaksanaan Program Magang di ITS?',
+    'Mahasiswa dalam melaksanakan magang harus memenuhi ketentuan berikut: 1. Pelaksanaan Program Magang memiliki durasi minimal 1 bulan dan maksimal 6 bulan. 2. Selama Program Magang berlangsung, mahasiswa tidak harus mengajukan cuti. 3. Mahasiswa secara penuh waktu bekerja di lapangan sesuai kesepakatan. 4. Mahasiswa bisa mendapatkan izin untuk kegiatan akademik tertentu dengan kesepakatan pihak Mitra Magang. 5. Mahasiswa dapat mengajukan konversi mata kuliah dengan CPMK yang selaras. 6. Diperlukan pembimbing internal dari Dosen Departemen dan pembimbing lapangan dari Mitra Magang. 7. Sebelum Program Magang, wajib menandatangani perjanjian kerjasama dan nota kesepahaman.',
+    '1. Mahasiswa mengurus surat rekomendasi departemen untuk pengajuan magang ke Mitra\n2. Mahasiswa mengajukan permohonan magang ke Mitra\n3. Mitra melakukan seleksi magang\n4. Mahasiswa menerima hasil seleksi magang dari Mitra\n5. Apabila tidak diterima, maka mahasiswa harus mengulang sejak langkah awal\n6. Apabila diterima, Mahasiswa melaporkan ke Departemen\n7. Mahasiswa/Departemen melakukan koordinasi dengan PK2 untuk pengurusan PKS dengan menyertakan Proposal Magang, Surat Rekomendasi Departemen dan Surat Penerimaan Magang\n8. Proses pengurusan PKS (Dapat dilakukan bersamaan dengan pelaksanaan Magang)',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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: 704 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: 6 tokens</li><li>mean: 16.22 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 57.55 tokens</li><li>max: 128 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                               | sentence_1                                                                                                                                                                                                                                                                                                                                                                                             |
+  |:-----------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Bagaimana cara menulis dokumen pemerintah dalam daftar pustaka?</code>             | <code>Nama pengarang dan tahun publikasi sama dengan penulisan artikel dari jurnal. Judul dokumen dicetak miring/italic. Volume atau nomor (jika ada). Nama penerbit. Kota tempat diterbitkan.</code>                                                                                                                                                                                                  |
+  | <code>Apa tugas dosen wali dalam pelaksanaan MBKM?</code>                                | <code>Dosen wali ditugaskan oleh Prodi untuk membuat perencanaan bersama dengan mahasiswa yang akan melaksanakan kegiatan MBKM, melakukan evaluasi terhadap kesesuaian bentuk dan lama pelaksanaan MBKM, serta melakukan penilaian atas rencana, pelaksanaan, dan evaluasi MBKM.</code>                                                                                                                |
+  | <code>Apa yang dimaksud dengan 'Hak Belajar Tiga Semester di Luar Program Studi'?</code> | <code>Hak Belajar Tiga Semester di Luar Program Studi adalah kebijakan yang memberikan mahasiswa kesempatan untuk satu semester (setara dengan 20 SKS) menempuh pembelajaran di luar program studi pada perguruan tinggi yang sama, dan paling lama dua semester (setara dengan 40 SKS) di program studi yang sama atau berbeda di perguruan tinggi yang berbeda atau di luar perguruan tinggi.</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"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 5
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `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
+- `num_train_epochs`: 5
+- `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
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True}
+- `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
+- `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_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Framework Versions
+- Python: 3.8.10
+- Sentence Transformers: 3.0.1
+- Transformers: 4.39.2
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.29.3
+- Datasets: 2.19.2
+- Tokenizers: 0.15.2
+
+## 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",
+}
+```
+
+#### 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 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.2",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d322a03f2a6639e63479205d02ed64fafcf97d072a2b490a3f11ca4fee946bcc
+size 470637416

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "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.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa685fc160bbdbab64058d4fc91b60e62d207e8dc60b9af5c002c5ab946ded00
+size 17083009

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "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": false,
+      "rstrip": false,
+      "single_word": false,
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  },
+  "bos_token": "<s>",
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+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 128,
+  "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": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

unigram.json

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