Add SetFit ABSA 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

@@ -0,0 +1,333 @@
+---
+library_name: setfit
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: firqaaa/indo-setfit-absa-bert-base-restaurants-aspect
+metrics:
+- accuracy
+widget:
+- text: basi:jujur game bagus n refresing banget klo pas masuk region sampe archon
+    quest kelar disayangkan basi utk kontennya diulang ulang ampe gila bener bener
+    membosankan login n menghabiskan resin temen ku udh gak main bosen emng marketing
+    game player biar ngerasain serunya game diawal tambahin konten end game gk kasih
+    resin tambahan biar yg dikerjain
+- text: karakter:game kikir pelit medit sumpah gacha ngak dapet tahan top up game
+    kemarin ngak kasih 1 karakter ngebahagiain player jgn download kalo mental aman
+- text: loading:nya loading screen element sampe 4 kali game sih ajg niat bikin game
+    ga
+- text: b5 kasih b5 playstore:hadiahnya plis karakter gratis b5 kasih b5 playstore
+- text: gb:update 10 gb udah 30 ditambah 10gb males
+pipeline_tag: text-classification
+inference: false
+---
+
+# SetFit Aspect Model with firqaaa/indo-setfit-absa-bert-base-restaurants-aspect
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [firqaaa/indo-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+This model was trained within the context of a larger system for ABSA, which looks like so:
+
+1. Use a spaCy model to select possible aspect span candidates.
+2. **Use this SetFit model to filter these possible aspect span candidates.**
+3. Use a SetFit model to classify the filtered aspect span candidates.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [firqaaa/indo-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **spaCy Model:** id_core_news_trf
+- **SetFitABSA Aspect Model:** [Funnyworld1412/ABSA_review_game_genshin_impact-aspect](https://huggingface.co/Funnyworld1412/ABSA_review_game_genshin_impact-aspect)
+- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_review_game_genshin_impact-polarity](https://huggingface.co/Funnyworld1412/ABSA_review_game_genshin_impact-polarity)
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label     | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| aspect    | <ul><li>'story:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'</li><li>'reward:tolong ditambah reward gachanya player kesulitan primo quest eksplorasi 100 dasar developer kapitalis game monoton ramah player kekurangan bahan gacha karakter'</li><li>'event:cuman saran pelit biar player gak kabur game sebelah hadiah event quest perbaiki udah nunggu event hadiah cuman gitu gitu aja sampek event selesai primogemnya 10 pull gacha gak tingakat kesulitan beda hadiah main kabur kalok pelit 1 jariang mohon perbaiki server indonya trimaksih'</li></ul>                                                                                                                                                                                                                                                                            |
+| no aspect | <ul><li>'saranku developer:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'</li><li>'story:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'</li><li>'kehilangan player player:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'</li></ul> |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import AbsaModel
+
+# Download from the 🤗 Hub
+model = AbsaModel.from_pretrained(
+    "Funnyworld1412/ABSA_review_game_genshin_impact-aspect",
+    "Funnyworld1412/ABSA_review_game_genshin_impact-polarity",
+)
+# Run inference
+preds = model("The food was great, but the venue is just way too busy.")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### 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 Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 4   | 31.2629 | 70  |
+
+| Label     | Training Sample Count |
+|:----------|:----------------------|
+| no aspect | 1049                  |
+| aspect    | 324                   |
+
+### Training Hyperparameters
+- batch_size: (4, 4)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0001 | 1    | 0.0089        | -               |
+| 0.0073 | 50   | 0.7206        | -               |
+| 0.0146 | 100  | 0.399         | -               |
+| 0.0218 | 150  | 0.0596        | -               |
+| 0.0291 | 200  | 0.3335        | -               |
+| 0.0364 | 250  | 0.1854        | -               |
+| 0.0437 | 300  | 0.0708        | -               |
+| 0.0510 | 350  | 0.0161        | -               |
+| 0.0583 | 400  | 0.3364        | -               |
+| 0.0655 | 450  | 0.0949        | -               |
+| 0.0728 | 500  | 0.1021        | -               |
+| 0.0801 | 550  | 0.3917        | -               |
+| 0.0874 | 600  | 0.0707        | -               |
+| 0.0947 | 650  | 0.3885        | -               |
+| 0.1020 | 700  | 0.046         | -               |
+| 0.1092 | 750  | 0.001         | -               |
+| 0.1165 | 800  | 0.0024        | -               |
+| 0.1238 | 850  | 0.2384        | -               |
+| 0.1311 | 900  | 0.0215        | -               |
+| 0.1384 | 950  | 0.2283        | -               |
+| 0.1457 | 1000 | 0.4564        | -               |
+| 0.1529 | 1050 | 0.0017        | -               |
+| 0.1602 | 1100 | 0.0612        | -               |
+| 0.1675 | 1150 | 0.2325        | -               |
+| 0.1748 | 1200 | 0.0568        | -               |
+| 0.1821 | 1250 | 0.0096        | -               |
+| 0.1894 | 1300 | 0.2803        | -               |
+| 0.1966 | 1350 | 0.0056        | -               |
+| 0.2039 | 1400 | 0.0107        | -               |
+| 0.2112 | 1450 | 0.0042        | -               |
+| 0.2185 | 1500 | 0.0636        | -               |
+| 0.2258 | 1550 | 0.0356        | -               |
+| 0.2331 | 1600 | 0.2264        | -               |
+| 0.2403 | 1650 | 0.2335        | -               |
+| 0.2476 | 1700 | 0.201         | -               |
+| 0.2549 | 1750 | 0.0386        | -               |
+| 0.2622 | 1800 | 0.0032        | -               |
+| 0.2695 | 1850 | 0.0023        | -               |
+| 0.2768 | 1900 | 0.0053        | -               |
+| 0.2840 | 1950 | 0.0228        | -               |
+| 0.2913 | 2000 | 0.0006        | -               |
+| 0.2986 | 2050 | 0.0003        | -               |
+| 0.3059 | 2100 | 0.0142        | -               |
+| 0.3132 | 2150 | 0.099         | -               |
+| 0.3205 | 2200 | 0.0144        | -               |
+| 0.3277 | 2250 | 0.0002        | -               |
+| 0.3350 | 2300 | 0.0042        | -               |
+| 0.3423 | 2350 | 0.0359        | -               |
+| 0.3496 | 2400 | 0.0004        | -               |
+| 0.3569 | 2450 | 0.0057        | -               |
+| 0.3642 | 2500 | 0.0046        | -               |
+| 0.3714 | 2550 | 0.0015        | -               |
+| 0.3787 | 2600 | 0.0023        | -               |
+| 0.3860 | 2650 | 0.0004        | -               |
+| 0.3933 | 2700 | 0.0002        | -               |
+| 0.4006 | 2750 | 0.0002        | -               |
+| 0.4079 | 2800 | 0.0267        | -               |
+| 0.4151 | 2850 | 0.0001        | -               |
+| 0.4224 | 2900 | 0.0003        | -               |
+| 0.4297 | 2950 | 0.0037        | -               |
+| 0.4370 | 3000 | 0.0005        | -               |
+| 0.4443 | 3050 | 0.0049        | -               |
+| 0.4516 | 3100 | 0.2431        | -               |
+| 0.4588 | 3150 | 0.2577        | -               |
+| 0.4661 | 3200 | 0.1556        | -               |
+| 0.4734 | 3250 | 0.1983        | -               |
+| 0.4807 | 3300 | 0.0884        | -               |
+| 0.4880 | 3350 | 0.0003        | -               |
+| 0.4953 | 3400 | 0.2302        | -               |
+| 0.5025 | 3450 | 0.0007        | -               |
+| 0.5098 | 3500 | 0.0002        | -               |
+| 0.5171 | 3550 | 0.0001        | -               |
+| 0.5244 | 3600 | 0.0845        | -               |
+| 0.5317 | 3650 | 0.0003        | -               |
+| 0.5390 | 3700 | 0.0001        | -               |
+| 0.5462 | 3750 | 0.0001        | -               |
+| 0.5535 | 3800 | 0.0           | -               |
+| 0.5608 | 3850 | 0.0001        | -               |
+| 0.5681 | 3900 | 0.001         | -               |
+| 0.5754 | 3950 | 0.0008        | -               |
+| 0.5827 | 4000 | 0.002         | -               |
+| 0.5899 | 4050 | 0.0002        | -               |
+| 0.5972 | 4100 | 0.1071        | -               |
+| 0.6045 | 4150 | 0.0001        | -               |
+| 0.6118 | 4200 | 0.0001        | -               |
+| 0.6191 | 4250 | 0.0001        | -               |
+| 0.6264 | 4300 | 0.0002        | -               |
+| 0.6336 | 4350 | 0.0001        | -               |
+| 0.6409 | 4400 | 0.0           | -               |
+| 0.6482 | 4450 | 0.2478        | -               |
+| 0.6555 | 4500 | 0.0           | -               |
+| 0.6628 | 4550 | 0.0003        | -               |
+| 0.6701 | 4600 | 0.0           | -               |
+| 0.6773 | 4650 | 0.0002        | -               |
+| 0.6846 | 4700 | 0.003         | -               |
+| 0.6919 | 4750 | 0.0007        | -               |
+| 0.6992 | 4800 | 0.0006        | -               |
+| 0.7065 | 4850 | 0.001         | -               |
+| 0.7138 | 4900 | 0.0106        | -               |
+| 0.7210 | 4950 | 0.0001        | -               |
+| 0.7283 | 5000 | 0.0002        | -               |
+| 0.7356 | 5050 | 0.0004        | -               |
+| 0.7429 | 5100 | 0.0008        | -               |
+| 0.7502 | 5150 | 0.0508        | -               |
+| 0.7575 | 5200 | 0.001         | -               |
+| 0.7647 | 5250 | 0.0           | -               |
+| 0.7720 | 5300 | 0.0249        | -               |
+| 0.7793 | 5350 | 0.0001        | -               |
+| 0.7866 | 5400 | 0.1026        | -               |
+| 0.7939 | 5450 | 0.0           | -               |
+| 0.8012 | 5500 | 0.0001        | -               |
+| 0.8084 | 5550 | 0.0028        | -               |
+| 0.8157 | 5600 | 0.0008        | -               |
+| 0.8230 | 5650 | 0.0002        | -               |
+| 0.8303 | 5700 | 0.0001        | -               |
+| 0.8376 | 5750 | 0.0           | -               |
+| 0.8449 | 5800 | 0.0001        | -               |
+| 0.8521 | 5850 | 0.0001        | -               |
+| 0.8594 | 5900 | 0.0094        | -               |
+| 0.8667 | 5950 | 0.0001        | -               |
+| 0.8740 | 6000 | 0.0           | -               |
+| 0.8813 | 6050 | 0.0           | -               |
+| 0.8886 | 6100 | 0.0           | -               |
+| 0.8958 | 6150 | 0.0001        | -               |
+| 0.9031 | 6200 | 0.0002        | -               |
+| 0.9104 | 6250 | 0.0026        | -               |
+| 0.9177 | 6300 | 0.1005        | -               |
+| 0.9250 | 6350 | 0.0002        | -               |
+| 0.9323 | 6400 | 0.0004        | -               |
+| 0.9395 | 6450 | 0.2456        | -               |
+| 0.9468 | 6500 | 0.0228        | -               |
+| 0.9541 | 6550 | 0.022         | -               |
+| 0.9614 | 6600 | 0.025         | -               |
+| 0.9687 | 6650 | 0.0002        | -               |
+| 0.9760 | 6700 | 0.0003        | -               |
+| 0.9832 | 6750 | 0.0001        | -               |
+| 0.9905 | 6800 | 0.0           | -               |
+| 0.9978 | 6850 | 0.1145        | -               |
+| 1.0    | 6865 | -             | 0.1868          |
+
+### Framework Versions
+- Python: 3.10.13
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- spaCy: 3.7.5
+- Transformers: 4.36.2
+- PyTorch: 2.1.2
+- Datasets: 2.19.2
+- Tokenizers: 0.15.2
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## 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 +1,47 @@
+{
+  "_name_or_path": "Funnyworld1412/ABSA_game_squad_busters-aspect",
+  "_num_labels": 5,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.36.2",
+    "pytorch": "2.1.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,9 @@
+{
+  "span_context": 0,
+  "spacy_model": "id_core_news_trf",
+  "normalize_embeddings": false,
+  "labels": [
+    "no aspect",
+    "aspect"
+  ]
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf1ee9ef054bda184b5c44f059daffee0fb7586f4c19abc8ef1ec8148fc14cb1
+size 497787752

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:878c3310043c54a265b6c250f628e7de1402fc24a1e1da866139d9e77f68c6ac
+size 6991

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": 512,
+  "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 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "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,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}