added onnx

README.md

@@ -2,44 +2,48 @@
 license: apache-2.0
 base_model: distilbert-base-cased
 tags:
-- generated_from_trainer
-- news_classification
-- multi_label
+  - generated_from_trainer
+  - news_classification
+  - multi_label
 datasets:
-- reuters21578
+  - reuters21578
 metrics:
-- f1
-- accuracy
+  - f1
+  - accuracy
 model-index:
-- name: distilbert-finetuned-reuters21578-multilabel
-  results:
-  - task:
-      name: Text Classification
-      type: text-classification
-    dataset:
-      name: reuters21578
-      type: reuters21578
-      config: ModApte
-      split: test
-      args: ModApte
-    metrics:
-    - name: F1
-      type: f1
-      value: 0.8628858578607322
-    - name: Accuracy
-      type: accuracy
-      value: 0.8195625759416768
+  - name: distilbert-finetuned-reuters21578-multilabel
+    results:
+      - task:
+          name: Text Classification
+          type: text-classification
+        dataset:
+          name: reuters21578
+          type: reuters21578
+          config: ModApte
+          split: test
+          args: ModApte
+        metrics:
+          - name: F1
+            type: f1
+            value: 0.8628858578607322
+          - name: Accuracy
+            type: accuracy
+            value: 0.8195625759416768
 language:
-- en
+  - en
 pipeline_tag: text-classification
 widget:
-- text: "JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWARDS The Ministry of International Trade and Industry (MITI) will revise its long-term energy supply/demand outlook by August to meet a forecast downtrend in Japanese energy demand, ministry officials said.     MITI is expected to lower the projection for primary energy supplies in the year 2000 to 550 mln kilolitres (kl) from 600 mln, they said.     The decision follows the emergence of structural changes in Japanese industry following the rise in the value of the yen and a decline in domestic electric power demand.     MITI is planning to work out a revised energy supply/demand outlook through deliberations of committee meetings of the Agency of Natural Resources and Energy, the officials said.     They said MITI will also review the breakdown of energy supply sources, including oil, nuclear, coal and natural gas.     Nuclear energy provided the bulk of Japan's electric power in the fiscal year ended March 31, supplying an estimated 27 pct on a kilowatt/hour basis, followed by oil (23 pct) and liquefied natural gas (21 pct), they noted.  REUTER"
-  example_title: "Example-1"
+  - text: "JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWARDS The Ministry of International Trade and Industry (MITI) will revise its long-term energy supply/demand outlook by August to meet a forecast downtrend in Japanese energy demand, ministry officials said.     MITI is expected to lower the projection for primary energy supplies in the year 2000 to 550 mln kilolitres (kl) from 600 mln, they said.     The decision follows the emergence of structural changes in Japanese industry following the rise in the value of the yen and a decline in domestic electric power demand.     MITI is planning to work out a revised energy supply/demand outlook through deliberations of committee meetings of the Agency of Natural Resources and Energy, the officials said.     They said MITI will also review the breakdown of energy supply sources, including oil, nuclear, coal and natural gas.     Nuclear energy provided the bulk of Japan's electric power in the fiscal year ended March 31, supplying an estimated 27 pct on a kilowatt/hour basis, followed by oil (23 pct) and liquefied natural gas (21 pct), they noted.  REUTER"
+    example_title: "Example-1"
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
+## Origin of this model
+
+This model was forked from https://huggingface.co/lxyuan/distilbert-finetuned-reuters21578-multilabel -- I just generated the onnx versions in /onnx
+
 ## Motivation
 
 Fine-tuning on the Reuters-21578 multilabel dataset is a valuable exercise, especially as it's frequently used in take-home tests during interviews. The dataset's complexity is just right for testing multilabel classification skills within a limited timeframe, while its real-world relevance helps simulate practical challenges. Experimenting with this dataset not only helps candidates prepare for interviews but also hones various skills including preprocessing, feature extraction, and model evaluation.
@@ -92,15 +96,14 @@ nat-gas 0.8600426316261292
 
 ```
 
-
 ## Overall Summary and Comparison Table
 
-| Metric                | Baseline (Scikit-learn) | Transformer Model |
-|-----------------------|--------------------------|-------------------|
-| Micro-Averaged F1      | 0.77                     | 0.86              |
-| Macro-Averaged F1      | 0.29                     | 0.33              |
-| Weighted Average F1    | 0.70                     | 0.84              |
-| Samples Average F1     | 0.75                     | 0.80              |
+| Metric              | Baseline (Scikit-learn) | Transformer Model |
+| ------------------- | ----------------------- | ----------------- |
+| Micro-Averaged F1   | 0.77                    | 0.86              |
+| Macro-Averaged F1   | 0.29                    | 0.33              |
+| Weighted Average F1 | 0.70                    | 0.84              |
+| Samples Average F1  | 0.75                    | 0.80              |
 
 **Precision vs Recall**: Both models prioritize high precision over recall. In our client-facing news classification model, precision takes precedence over recall. This is because the repercussions of false positives are more severe and harder to justify to clients compared to false negatives. When the model incorrectly tags a news item with a topic, it's challenging to explain this error. On the other hand, if the model misses a topic, it's easier to defend by stating that the topic wasn't sufficiently emphasized in the news article.
 
@@ -112,7 +115,6 @@ nat-gas 0.8600426316261292
 
 **Conclusion**: While both models exhibit high precision, which is a business requirement, the transformer model slightly outperforms the scikit-learn baseline model in all metrics considered. It provides a better trade-off between precision and recall, as well as some improvement, albeit small, in handling minority classes. Thus, despite sharing similar weaknesses with the baseline, the transformer model demonstrates incremental improvements that could be significant in a production setting.
 
-
 ## Training and evaluation data
 
 We remove single appearance label from both training and test sets using the following code:
@@ -150,7 +152,6 @@ print(f"We have {len(unique_labels)} unique labels:\n{unique_labels}")
 {'veg-oil', 'gold', 'platinum', 'ipi', 'acq', 'carcass', 'wool', 'coconut-oil', 'linseed', 'copper', 'soy-meal', 'jet', 'dlr', 'copra-cake', 'hog', 'rand', 'strategic-metal', 'can', 'tea', 'sorghum', 'livestock', 'barley', 'lumber', 'earn', 'wheat', 'trade', 'soy-oil', 'cocoa', 'inventories', 'income', 'rubber', 'tin', 'iron-steel', 'ship', 'rapeseed', 'wpi', 'sun-oil', 'pet-chem', 'palmkernel', 'nat-gas', 'gnp', 'l-cattle', 'propane', 'rice', 'lead', 'alum', 'instal-debt', 'saudriyal', 'cpu', 'jobs', 'meal-feed', 'oilseed', 'dmk', 'plywood', 'zinc', 'retail', 'dfl', 'cpi', 'crude', 'pork-belly', 'gas', 'money-fx', 'corn', 'tapioca', 'palladium', 'lei', 'cornglutenfeed', 'sunseed', 'potato', 'silver', 'sugar', 'grain', 'groundnut', 'naphtha', 'orange', 'soybean', 'coconut', 'stg', 'cotton', 'yen', 'rape-oil', 'palm-oil', 'oat', 'reserves', 'housing', 'interest', 'coffee', 'fuel', 'austdlr', 'money-supply', 'heat', 'fishmeal', 'bop', 'nickel', 'nzdlr'}
 ```
 
-
 ## Training procedure
 
 [EDA on Reuters-21578 dataset](https://github.com/LxYuan0420/nlp/blob/main/notebooks/eda_reuters.ipynb):
@@ -164,12 +165,14 @@ This notebook delves into advanced text classification using a Transformer model
 
 [Multilabel Stratified Sampling & Hypyerparameter Search on Reuters Dataset](https://github.com/LxYuan0420/nlp/blob/main/notebooks/transformer_reuters_hyperparameter_tuning.ipynb):
 In this notebook, we explore advanced machine learning techniques through the lens of the Hugging Face Trainer API, specifically targeting Multilabel Iterative Stratified Splitting and Hyperparameter Search. The former aims to fairly distribute imbalanced datasets across multiple labels in k-fold cross-validation, maintaining a distribution closely resembling that of the complete dataset. The latter walks users through a structured hyperparameter search to fine-tune model performance for optimal results.
+
 ## Evaluation results
+
 <details>
 <summary>Transformer Model Evaluation Result</summary>
 
 Classification Report:
-                  precision    recall  f1-score   support
+precision recall f1-score support
 
             acq       0.97      0.93      0.95       719
            alum       1.00      0.70      0.82        23
@@ -269,11 +272,11 @@ Classification Report:
 
       micro avg       0.92      0.81      0.86      3694
       macro avg       0.41      0.30      0.33      3694
-   weighted avg       0.87      0.81      0.84      3694
-    samples avg       0.81      0.80      0.80      3694
 
-</details>
+weighted avg 0.87 0.81 0.84 3694
+samples avg 0.81 0.80 0.80 3694
 
+</details>
 
 <details>
 <summary>Scikit-learn Baseline Model Evaluation Result</summary>
@@ -378,14 +381,16 @@ Classification Report:
 
       micro avg       0.97      0.64      0.77      3694
       macro avg       0.98      0.25      0.29      3694
-   weighted avg       0.96      0.64      0.70      3694
-    samples avg       0.98      0.74      0.75      3694
+
+weighted avg 0.96 0.64 0.70 3694
+samples avg 0.98 0.74 0.75 3694
+
 </details>
 
-  
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
+
 - learning_rate: 2e-05
 - train_batch_size: 32
 - eval_batch_size: 32
@@ -396,33 +401,32 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
-| 0.1801        | 1.0   | 300  | 0.0439          | 0.3896 | 0.6210  | 0.3566   |
-| 0.0345        | 2.0   | 600  | 0.0287          | 0.6289 | 0.7318  | 0.5954   |
-| 0.0243        | 3.0   | 900  | 0.0219          | 0.6721 | 0.7579  | 0.6084   |
-| 0.0178        | 4.0   | 1200 | 0.0177          | 0.7505 | 0.8128  | 0.6908   |
-| 0.014         | 5.0   | 1500 | 0.0151          | 0.7905 | 0.8376  | 0.7278   |
-| 0.0115        | 6.0   | 1800 | 0.0135          | 0.8132 | 0.8589  | 0.7555   |
-| 0.0096        | 7.0   | 2100 | 0.0124          | 0.8291 | 0.8727  | 0.7725   |
-| 0.0082        | 8.0   | 2400 | 0.0124          | 0.8335 | 0.8757  | 0.7822   |
-| 0.0071        | 9.0   | 2700 | 0.0119          | 0.8392 | 0.8847  | 0.7883   |
-| 0.0064        | 10.0  | 3000 | 0.0123          | 0.8339 | 0.8810  | 0.7828   |
-| 0.0058        | 11.0  | 3300 | 0.0114          | 0.8538 | 0.8999  | 0.8047   |
-| 0.0053        | 12.0  | 3600 | 0.0113          | 0.8525 | 0.8967  | 0.8044   |
-| 0.0048        | 13.0  | 3900 | 0.0115          | 0.8520 | 0.8982  | 0.8029   |
-| 0.0045        | 14.0  | 4200 | 0.0111          | 0.8566 | 0.8962  | 0.8104   |
-| 0.0042        | 15.0  | 4500 | 0.0110          | 0.8610 | 0.9060  | 0.8165   |
-| 0.0039        | 16.0  | 4800 | 0.0112          | 0.8583 | 0.9021  | 0.8138   |
-| 0.0037        | 17.0  | 5100 | 0.0110          | 0.8620 | 0.9055  | 0.8196   |
-| 0.0035        | 18.0  | 5400 | 0.0110          | 0.8629 | 0.9063  | 0.8196   |
-| 0.0035        | 19.0  | 5700 | 0.0111          | 0.8624 | 0.9062  | 0.8180   |
-| 0.0034        | 20.0  | 6000 | 0.0111          | 0.8626 | 0.9055  | 0.8177   |
-
+| Training Loss | Epoch | Step | Validation Loss |   F1   | Roc Auc | Accuracy |
+| :-----------: | :---: | :--: | :-------------: | :----: | :-----: | :------: |
+|    0.1801     |  1.0  | 300  |     0.0439      | 0.3896 | 0.6210  |  0.3566  |
+|    0.0345     |  2.0  | 600  |     0.0287      | 0.6289 | 0.7318  |  0.5954  |
+|    0.0243     |  3.0  | 900  |     0.0219      | 0.6721 | 0.7579  |  0.6084  |
+|    0.0178     |  4.0  | 1200 |     0.0177      | 0.7505 | 0.8128  |  0.6908  |
+|     0.014     |  5.0  | 1500 |     0.0151      | 0.7905 | 0.8376  |  0.7278  |
+|    0.0115     |  6.0  | 1800 |     0.0135      | 0.8132 | 0.8589  |  0.7555  |
+|    0.0096     |  7.0  | 2100 |     0.0124      | 0.8291 | 0.8727  |  0.7725  |
+|    0.0082     |  8.0  | 2400 |     0.0124      | 0.8335 | 0.8757  |  0.7822  |
+|    0.0071     |  9.0  | 2700 |     0.0119      | 0.8392 | 0.8847  |  0.7883  |
+|    0.0064     | 10.0  | 3000 |     0.0123      | 0.8339 | 0.8810  |  0.7828  |
+|    0.0058     | 11.0  | 3300 |     0.0114      | 0.8538 | 0.8999  |  0.8047  |
+|    0.0053     | 12.0  | 3600 |     0.0113      | 0.8525 | 0.8967  |  0.8044  |
+|    0.0048     | 13.0  | 3900 |     0.0115      | 0.8520 | 0.8982  |  0.8029  |
+|    0.0045     | 14.0  | 4200 |     0.0111      | 0.8566 | 0.8962  |  0.8104  |
+|    0.0042     | 15.0  | 4500 |     0.0110      | 0.8610 | 0.9060  |  0.8165  |
+|    0.0039     | 16.0  | 4800 |     0.0112      | 0.8583 | 0.9021  |  0.8138  |
+|    0.0037     | 17.0  | 5100 |     0.0110      | 0.8620 | 0.9055  |  0.8196  |
+|    0.0035     | 18.0  | 5400 |     0.0110      | 0.8629 | 0.9063  |  0.8196  |
+|    0.0035     | 19.0  | 5700 |     0.0111      | 0.8624 | 0.9062  |  0.8180  |
+|    0.0034     | 20.0  | 6000 |     0.0111      | 0.8626 | 0.9055  |  0.8177  |
 
 ### Framework versions
 
 - Transformers 4.33.0.dev0
 - Pytorch 2.0.1+cu117
 - Datasets 2.14.3
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3

onnx/config.json

@@ -0,0 +1,220 @@
+{
+  "_name_or_path": "lxyuan/distilbert-finetuned-reuters21578-multilabel",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "acq",
+    "1": "alum",
+    "2": "austdlr",
+    "3": "barley",
+    "4": "bop",
+    "5": "can",
+    "6": "carcass",
+    "7": "cocoa",
+    "8": "coconut",
+    "9": "coconut-oil",
+    "10": "coffee",
+    "11": "copper",
+    "12": "copra-cake",
+    "13": "corn",
+    "14": "cornglutenfeed",
+    "15": "cotton",
+    "16": "cpi",
+    "17": "cpu",
+    "18": "crude",
+    "19": "dfl",
+    "20": "dlr",
+    "21": "dmk",
+    "22": "earn",
+    "23": "fishmeal",
+    "24": "fuel",
+    "25": "gas",
+    "26": "gnp",
+    "27": "gold",
+    "28": "grain",
+    "29": "groundnut",
+    "30": "heat",
+    "31": "hog",
+    "32": "housing",
+    "33": "income",
+    "34": "instal-debt",
+    "35": "interest",
+    "36": "inventories",
+    "37": "ipi",
+    "38": "iron-steel",
+    "39": "jet",
+    "40": "jobs",
+    "41": "l-cattle",
+    "42": "lead",
+    "43": "lei",
+    "44": "linseed",
+    "45": "livestock",
+    "46": "lumber",
+    "47": "meal-feed",
+    "48": "money-fx",
+    "49": "money-supply",
+    "50": "naphtha",
+    "51": "nat-gas",
+    "52": "nickel",
+    "53": "nzdlr",
+    "54": "oat",
+    "55": "oilseed",
+    "56": "orange",
+    "57": "palladium",
+    "58": "palm-oil",
+    "59": "palmkernel",
+    "60": "pet-chem",
+    "61": "platinum",
+    "62": "plywood",
+    "63": "pork-belly",
+    "64": "potato",
+    "65": "propane",
+    "66": "rand",
+    "67": "rape-oil",
+    "68": "rapeseed",
+    "69": "reserves",
+    "70": "retail",
+    "71": "rice",
+    "72": "rubber",
+    "73": "saudriyal",
+    "74": "ship",
+    "75": "silver",
+    "76": "sorghum",
+    "77": "soy-meal",
+    "78": "soy-oil",
+    "79": "soybean",
+    "80": "stg",
+    "81": "strategic-metal",
+    "82": "sugar",
+    "83": "sun-oil",
+    "84": "sunseed",
+    "85": "tapioca",
+    "86": "tea",
+    "87": "tin",
+    "88": "trade",
+    "89": "veg-oil",
+    "90": "wheat",
+    "91": "wool",
+    "92": "wpi",
+    "93": "yen",
+    "94": "zinc"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "acq": 0,
+    "alum": 1,
+    "austdlr": 2,
+    "barley": 3,
+    "bop": 4,
+    "can": 5,
+    "carcass": 6,
+    "cocoa": 7,
+    "coconut": 8,
+    "coconut-oil": 9,
+    "coffee": 10,
+    "copper": 11,
+    "copra-cake": 12,
+    "corn": 13,
+    "cornglutenfeed": 14,
+    "cotton": 15,
+    "cpi": 16,
+    "cpu": 17,
+    "crude": 18,
+    "dfl": 19,
+    "dlr": 20,
+    "dmk": 21,
+    "earn": 22,
+    "fishmeal": 23,
+    "fuel": 24,
+    "gas": 25,
+    "gnp": 26,
+    "gold": 27,
+    "grain": 28,
+    "groundnut": 29,
+    "heat": 30,
+    "hog": 31,
+    "housing": 32,
+    "income": 33,
+    "instal-debt": 34,
+    "interest": 35,
+    "inventories": 36,
+    "ipi": 37,
+    "iron-steel": 38,
+    "jet": 39,
+    "jobs": 40,
+    "l-cattle": 41,
+    "lead": 42,
+    "lei": 43,
+    "linseed": 44,
+    "livestock": 45,
+    "lumber": 46,
+    "meal-feed": 47,
+    "money-fx": 48,
+    "money-supply": 49,
+    "naphtha": 50,
+    "nat-gas": 51,
+    "nickel": 52,
+    "nzdlr": 53,
+    "oat": 54,
+    "oilseed": 55,
+    "orange": 56,
+    "palladium": 57,
+    "palm-oil": 58,
+    "palmkernel": 59,
+    "pet-chem": 60,
+    "platinum": 61,
+    "plywood": 62,
+    "pork-belly": 63,
+    "potato": 64,
+    "propane": 65,
+    "rand": 66,
+    "rape-oil": 67,
+    "rapeseed": 68,
+    "reserves": 69,
+    "retail": 70,
+    "rice": 71,
+    "rubber": 72,
+    "saudriyal": 73,
+    "ship": 74,
+    "silver": 75,
+    "sorghum": 76,
+    "soy-meal": 77,
+    "soy-oil": 78,
+    "soybean": 79,
+    "stg": 80,
+    "strategic-metal": 81,
+    "sugar": 82,
+    "sun-oil": 83,
+    "sunseed": 84,
+    "tapioca": 85,
+    "tea": 86,
+    "tin": 87,
+    "trade": 88,
+    "veg-oil": 89,
+    "wheat": 90,
+    "wool": 91,
+    "wpi": 92,
+    "yen": 93,
+    "zinc": 94
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "multi_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.1",
+  "vocab_size": 28996
+}

onnx/model.onnx

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+size 263553912

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onnx/ort_config.json

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+{
+  "one_external_file": true,
+  "opset": null,
+  "optimization": {},
+  "optimum_version": "1.16.1",
+  "quantization": {
+    "activations_dtype": "QUInt8",
+    "activations_symmetric": false,
+    "format": "QOperator",
+    "is_static": false,
+    "mode": "IntegerOps",
+    "nodes_to_exclude": [],
+    "nodes_to_quantize": [],
+    "operators_to_quantize": [
+      "Conv",
+      "MatMul",
+      "Attention",
+      "LSTM",
+      "Gather",
+      "Transpose",
+      "EmbedLayerNormalization"
+    ],
+    "per_channel": false,
+    "qdq_add_pair_to_weight": false,
+    "qdq_dedicated_pair": false,
+    "qdq_op_type_per_channel_support_to_axis": {
+      "MatMul": 1
+    },
+    "reduce_range": false,
+    "weights_dtype": "QInt8",
+    "weights_symmetric": true
+  },
+  "transformers_version": "4.36.1",
+  "use_external_data_format": false
+}

onnx/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
+  }
+}

onnx/tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "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": "DistilBertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}