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Mingyi
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classify_title_subject

Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
Inference Endpoints
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add model

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  1. README.md +97 -11
  2. config.json +23 -16
  3. tf_model.h5 +2 -2
README.md CHANGED
@@ -1,15 +1,101 @@
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- This model classifies the title of a content (e.g., YouTube video, article, or podcast episode) into 1 of 8 subjects
 
 
 
 
 
 
 
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- 0. art
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- 1. personal development
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- 2. world
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- 3. health
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- 4. science
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- 5. business
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- 6. humanities
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- 7. technology.
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- This model is fine-tuned from **distilbert-base-uncased** using ~1k labeled data.
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- This model is used to support [Sanderling](https://sanderling.app)
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_keras_callback
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+ model-index:
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+ - name: tmp6tsjsfbf
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+ results: []
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+ ---
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+ <!-- This model card has been generated automatically according to the information Keras had access to. You should
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+ probably proofread and complete it, then remove this comment. -->
 
 
 
 
 
 
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+ # tmp6tsjsfbf
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+ This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Train Loss: 0.0178
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+ - Train Sparse Categorical Accuracy: 0.9962
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+ - Epoch: 49
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - optimizer: {'name': 'Adam', 'learning_rate': 5e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
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+ - training_precision: float32
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+
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+ ### Training results
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+
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+ | Train Loss | Train Sparse Categorical Accuracy | Epoch |
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+ |:----------:|:---------------------------------:|:-----:|
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+ | 1.8005 | 0.3956 | 0 |
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+ | 1.3302 | 0.5916 | 1 |
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+ | 0.8998 | 0.7575 | 2 |
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+ | 0.6268 | 0.8468 | 3 |
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+ | 0.4239 | 0.9062 | 4 |
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+ | 0.2982 | 0.9414 | 5 |
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+ | 0.2245 | 0.9625 | 6 |
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+ | 0.1678 | 0.9730 | 7 |
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+ | 0.1399 | 0.9745 | 8 |
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+ | 0.1059 | 0.9827 | 9 |
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+ | 0.0822 | 0.9850 | 10 |
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+ | 0.0601 | 0.9902 | 11 |
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+ | 0.0481 | 0.9932 | 12 |
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+ | 0.0386 | 0.9955 | 13 |
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+ | 0.0292 | 0.9977 | 14 |
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+ | 0.0353 | 0.9940 | 15 |
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+ | 0.0336 | 0.9932 | 16 |
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+ | 0.0345 | 0.9910 | 17 |
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+ | 0.0179 | 0.9985 | 18 |
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+ | 0.0150 | 0.9985 | 19 |
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+ | 0.0365 | 0.9895 | 20 |
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+ | 0.0431 | 0.9895 | 21 |
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+ | 0.0243 | 0.9955 | 22 |
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+ | 0.0317 | 0.9925 | 23 |
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+ | 0.0375 | 0.9902 | 24 |
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+ | 0.0138 | 0.9970 | 25 |
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+ | 0.0159 | 0.9977 | 26 |
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+ | 0.0160 | 0.9962 | 27 |
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+ | 0.0151 | 0.9977 | 28 |
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+ | 0.0337 | 0.9902 | 29 |
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+ | 0.0119 | 0.9977 | 30 |
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+ | 0.0165 | 0.9955 | 31 |
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+ | 0.0133 | 0.9977 | 32 |
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+ | 0.0047 | 1.0 | 33 |
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+ | 0.0037 | 1.0 | 34 |
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+ | 0.0033 | 1.0 | 35 |
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+ | 0.0031 | 1.0 | 36 |
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+ | 0.0036 | 1.0 | 37 |
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+ | 0.0343 | 0.9887 | 38 |
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+ | 0.0234 | 0.9962 | 39 |
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+ | 0.0034 | 1.0 | 40 |
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+ | 0.0036 | 1.0 | 41 |
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+ | 0.0261 | 0.9917 | 42 |
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+ | 0.0111 | 0.9970 | 43 |
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+ | 0.0039 | 1.0 | 44 |
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+ | 0.0214 | 0.9932 | 45 |
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+ | 0.0044 | 0.9985 | 46 |
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+ | 0.0122 | 0.9985 | 47 |
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+ | 0.0119 | 0.9962 | 48 |
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+ | 0.0178 | 0.9962 | 49 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.15.0
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+ - TensorFlow 2.7.0
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+ - Tokenizers 0.10.3
config.json CHANGED
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  {
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- "_name_or_path": "distilbert-base-uncased",
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- "activation": "gelu",
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  "architectures": [
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- "DistilBertForSequenceClassification"
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  ],
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- "attention_dropout": 0.1,
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- "dim": 768,
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- "dropout": 0.1,
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- "hidden_dim": 3072,
 
 
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  "id2label": {
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  "0": "LABEL_0",
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  "1": "LABEL_1",
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  "7": "LABEL_7"
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  },
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  "initializer_range": 0.02,
 
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  "label2id": {
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  "LABEL_0": 0,
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  "LABEL_1": 1,
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  "LABEL_6": 6,
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  "LABEL_7": 7
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  },
 
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  "max_position_embeddings": 512,
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- "model_type": "distilbert",
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- "n_heads": 12,
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- "n_layers": 6,
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  "pad_token_id": 0,
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- "qa_dropout": 0.1,
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- "seq_classif_dropout": 0.2,
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- "sinusoidal_pos_embds": false,
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- "tie_weights_": true,
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- "transformers_version": "4.12.3",
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- "vocab_size": 30522
 
 
 
 
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  }
 
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  {
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+ "_name_or_path": "bert-base-multilingual-cased",
 
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  "architectures": [
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+ "BertForSequenceClassification"
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  ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "directionality": "bidi",
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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  "id2label": {
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  "0": "LABEL_0",
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  "1": "LABEL_1",
 
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  "7": "LABEL_7"
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  },
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  "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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  "label2id": {
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  "LABEL_0": 0,
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  "LABEL_1": 1,
 
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  "LABEL_6": 6,
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  "LABEL_7": 7
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  },
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+ "layer_norm_eps": 1e-12,
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  "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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  "pad_token_id": 0,
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+ "pooler_fc_size": 768,
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+ "pooler_num_attention_heads": 12,
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+ "pooler_num_fc_layers": 3,
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+ "pooler_size_per_head": 128,
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+ "pooler_type": "first_token_transform",
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.15.0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 119547
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  }
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