Add new SentenceTransformer model.
Browse files- 1_Pooling/config.json +9 -0
- README.md +130 -0
- config.json +28 -0
- config_sentence_transformers.json +7 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +51 -0
- tokenizer.json +2975 -0
- tokenizer_config.json +54 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false
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}
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README.md
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---
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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---
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# Oztobuzz/Simcse_test_banking
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('Oztobuzz/Simcse_test_banking')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Usage (HuggingFace Transformers)
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Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('Oztobuzz/Simcse_test_banking')
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model = AutoModel.from_pretrained('Oztobuzz/Simcse_test_banking')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Oztobuzz/Simcse_test_banking)
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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 102 with parameters:
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```
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{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
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```
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{'scale': 20.0, 'similarity_fct': 'cos_sim'}
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```
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Parameters of the fit()-Method:
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```
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{
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"epochs": 1,
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"evaluation_steps": 0,
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"evaluator": "NoneType",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"lr": 5e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 11,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: RobertaModel
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(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})
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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config.json
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{
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"_name_or_path": "Oztobuzz/my_testing_mlm_model",
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"architectures": [
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"RobertaModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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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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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 258,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"tokenizer_class": "PhobertTokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 64001
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.2.2",
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"transformers": "4.35.2",
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"pytorch": "2.1.0+cu121"
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}
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:07e6765740839d2bf6330d7758317881e72517eb8e6179c6c0c2694409fc9b1a
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size 540015464
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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}
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]
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sentence_bert_config.json
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{
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"max_seq_length": 32,
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"do_lower_case": false
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"cls_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "<mask>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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| 45 |
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"content": "<unk>",
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| 46 |
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"lstrip": false,
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| 47 |
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"normalized": false,
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"rstrip": false,
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| 49 |
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"single_word": false
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}
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}
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tokenizer.json
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| 1 |
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| 2 |
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| 3 |
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| 4 |
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| 9 |
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| 11 |
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| 12 |
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| 15 |
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| 16 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 65 |
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 74 |
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| 76 |
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| 77 |
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| 82 |
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| 83 |
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| 84 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 96 |
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| 103 |
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| 108 |
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| 115 |
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| 126 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 186 |
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| 189 |
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| 190 |
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| 1748 |
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| 1749 |
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| 1750 |
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| 1753 |
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| 1754 |
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| 1755 |
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| 1756 |
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| 1757 |
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| 1758 |
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| 1759 |
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| 1760 |
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| 1761 |
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| 1762 |
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| 1763 |
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| 1764 |
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| 1765 |
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| 1766 |
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|
| 1767 |
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| 1768 |
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|
| 1769 |
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|
| 1770 |
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|
| 1771 |
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|
| 1772 |
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|
| 1773 |
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|
| 1774 |
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|
| 1775 |
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|
| 1776 |
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|
| 1777 |
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|
| 1778 |
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|
| 1779 |
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|
| 1780 |
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|
| 1781 |
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|
| 1782 |
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|
| 1783 |
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|
| 1784 |
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|
| 1785 |
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|
| 1786 |
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|
| 1787 |
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|
| 1788 |
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|
| 1789 |
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|
| 1790 |
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|
| 1791 |
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|
| 1792 |
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|
| 1793 |
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|
| 1794 |
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|
| 1795 |
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|
| 1796 |
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|
| 1797 |
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|
| 1798 |
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|
| 1799 |
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|
| 1800 |
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|
| 1801 |
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|
| 1802 |
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|
| 1803 |
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|
| 1804 |
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|
| 1805 |
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|
| 1806 |
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|
| 1807 |
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|
| 1808 |
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|
| 1809 |
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|
| 1810 |
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|
| 1811 |
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|
| 1812 |
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|
| 1813 |
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|
| 1814 |
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|
| 1815 |
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|
| 1816 |
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|
| 1817 |
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|
| 1818 |
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|
| 1819 |
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|
| 1820 |
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|
| 1821 |
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|
| 1822 |
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|
| 1823 |
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|
| 1824 |
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|
| 1825 |
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|
| 1826 |
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|
| 1827 |
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|
| 1828 |
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|
| 1829 |
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|
| 1830 |
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|
| 1831 |
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|
| 1832 |
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|
| 1833 |
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|
| 1834 |
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|
| 1835 |
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|
| 1836 |
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|
| 1837 |
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|
| 1838 |
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|
| 1839 |
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|
| 1840 |
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|
| 1841 |
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|
| 1842 |
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|
| 1843 |
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|
| 1844 |
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|
| 1845 |
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|
| 1846 |
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|
| 1847 |
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|
| 1848 |
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|
| 1849 |
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|
| 1850 |
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"nott": 1656,
|
| 1851 |
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"oh": 1657,
|
| 1852 |
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|
| 1853 |
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"re": 1659,
|
| 1854 |
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|
| 1855 |
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|
| 1856 |
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|
| 1857 |
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|
| 1858 |
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|
| 1859 |
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"tw": 1665,
|
| 1860 |
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|
| 1861 |
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|
| 1862 |
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|
| 1863 |
+
"ula": 1669,
|
| 1864 |
+
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|
| 1865 |
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|
| 1866 |
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|
| 1867 |
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|
| 1868 |
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|
| 1869 |
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|
| 1870 |
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|
| 1871 |
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|
| 1872 |
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|
| 1873 |
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|
| 1874 |
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|
| 1875 |
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|
| 1876 |
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|
| 1877 |
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|
| 1878 |
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|
| 1879 |
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|
| 1880 |
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|
| 1881 |
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|
| 1882 |
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|
| 1883 |
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|
| 1884 |
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|
| 1885 |
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|
| 1886 |
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|
| 1887 |
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|
| 1888 |
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|
| 1889 |
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|
| 1890 |
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|
| 1891 |
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|
| 1892 |
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|
| 1893 |
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| 1895 |
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| 1896 |
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| 1897 |
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| 1898 |
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|
| 1899 |
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| 1900 |
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| 1901 |
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| 1902 |
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| 1903 |
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| 1904 |
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| 1905 |
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| 1906 |
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| 1907 |
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| 1908 |
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| 1909 |
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| 1910 |
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|
| 1911 |
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| 1912 |
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| 1913 |
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| 1914 |
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| 1915 |
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|
| 1916 |
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| 1917 |
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|
| 1918 |
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|
| 1919 |
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|
| 1920 |
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|
| 1921 |
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| 1922 |
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|
| 1923 |
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|
| 1924 |
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|
| 1925 |
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|
| 1926 |
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|
| 1927 |
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|
| 1928 |
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|
| 1929 |
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|
| 1930 |
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|
| 1931 |
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|
| 1932 |
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|
| 1933 |
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|
| 1934 |
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|
| 1935 |
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|
| 1936 |
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|
| 1937 |
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|
| 1938 |
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|
| 1939 |
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|
| 1940 |
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|
| 1941 |
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|
| 1942 |
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|
| 1943 |
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|
| 1944 |
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|
| 1945 |
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|
| 1946 |
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|
| 1947 |
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|
| 1948 |
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|
| 1949 |
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|
| 1950 |
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|
| 1951 |
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|
| 1952 |
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|
| 1953 |
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|
| 1954 |
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|
| 1955 |
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|
| 1956 |
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|
| 1957 |
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|
| 1958 |
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|
| 1959 |
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|
| 1960 |
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| 1961 |
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| 1962 |
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| 1963 |
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| 1964 |
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| 1965 |
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|
| 1966 |
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|
| 1967 |
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|
| 1968 |
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|
| 1969 |
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| 1970 |
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| 1971 |
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| 1972 |
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| 1973 |
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| 1974 |
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| 1975 |
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|
| 1976 |
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|
| 1977 |
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| 1978 |
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|
| 1979 |
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| 1980 |
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|
| 1981 |
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|
| 1982 |
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|
| 1983 |
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|
| 1984 |
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|
| 1985 |
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|
| 1986 |
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|
| 1987 |
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|
| 1988 |
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|
| 1989 |
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|
| 1990 |
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|
| 1991 |
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| 1992 |
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| 1993 |
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|
| 1994 |
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|
| 1995 |
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|
| 1996 |
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|
| 1997 |
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| 1998 |
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|
| 1999 |
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|
| 2000 |
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|
| 2001 |
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| 2002 |
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|
| 2003 |
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|
| 2004 |
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|
| 2005 |
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| 2006 |
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| 2007 |
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| 2008 |
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|
| 2009 |
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| 2010 |
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| 2011 |
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|
| 2012 |
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|
| 2013 |
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| 2014 |
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| 2015 |
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| 2016 |
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| 2017 |
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| 2018 |
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| 2019 |
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| 2020 |
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| 2023 |
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| 2024 |
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| 2025 |
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| 2026 |
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| 2027 |
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| 2036 |
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| 2037 |
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| 2038 |
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| 2039 |
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| 2040 |
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| 2041 |
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| 2048 |
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| 2062 |
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| 2072 |
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| 2077 |
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| 2078 |
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| 2079 |
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| 2088 |
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| 2089 |
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| 2090 |
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| 2091 |
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| 2096 |
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| 2097 |
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| 2098 |
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| 2099 |
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| 2100 |
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| 2101 |
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| 2102 |
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| 2103 |
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| 2104 |
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| 2105 |
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| 2106 |
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| 2107 |
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| 2108 |
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| 2109 |
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| 2110 |
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| 2111 |
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| 2112 |
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| 2113 |
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| 2114 |
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| 2115 |
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| 2116 |
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| 2117 |
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| 2118 |
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| 2119 |
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| 2120 |
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| 2121 |
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| 2122 |
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| 2123 |
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| 2124 |
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| 2125 |
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| 2126 |
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| 2127 |
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| 2128 |
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| 2129 |
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| 2130 |
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| 2131 |
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| 2132 |
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| 2133 |
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| 2134 |
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| 2135 |
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| 2136 |
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|
| 2137 |
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| 2138 |
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| 2139 |
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|
| 2140 |
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| 2141 |
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| 2142 |
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| 2143 |
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|
| 2144 |
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|
| 2145 |
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|
| 2146 |
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| 2147 |
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|
| 2148 |
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|
| 2149 |
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|
| 2150 |
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|
| 2151 |
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|
| 2152 |
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|
| 2153 |
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|
| 2154 |
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|
| 2155 |
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|
| 2156 |
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|
| 2157 |
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|
| 2158 |
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|
| 2159 |
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|
| 2160 |
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|
| 2161 |
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|
| 2162 |
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|
| 2163 |
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|
| 2164 |
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|
| 2165 |
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|
| 2166 |
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|
| 2167 |
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|
| 2168 |
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|
| 2169 |
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|
| 2170 |
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|
| 2171 |
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|
| 2172 |
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|
| 2173 |
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|
| 2174 |
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|
| 2175 |
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|
| 2176 |
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|
| 2177 |
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|
| 2178 |
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|
| 2179 |
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|
| 2180 |
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|
| 2181 |
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|
| 2182 |
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|
| 2183 |
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|
| 2184 |
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|
| 2185 |
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|
| 2186 |
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|
| 2187 |
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|
| 2188 |
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|
| 2189 |
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|
| 2190 |
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|
| 2191 |
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|
| 2192 |
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|
| 2193 |
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| 2194 |
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|
| 2195 |
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|
| 2196 |
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| 2197 |
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| 2198 |
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| 2199 |
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|
| 2200 |
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|
| 2201 |
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|
| 2202 |
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|
| 2203 |
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|
| 2204 |
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|
| 2205 |
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|
| 2206 |
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|
| 2207 |
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|
| 2208 |
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|
| 2209 |
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|
| 2210 |
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|
| 2211 |
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|
| 2212 |
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|
| 2213 |
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|
| 2214 |
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|
| 2215 |
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|
| 2216 |
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|
| 2217 |
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|
| 2218 |
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|
| 2219 |
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|
| 2220 |
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|
| 2221 |
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|
| 2222 |
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| 2223 |
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| 2224 |
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| 2225 |
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| 2226 |
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|
| 2227 |
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|
| 2228 |
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| 2229 |
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|
| 2230 |
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|
| 2231 |
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|
| 2232 |
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|
| 2233 |
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|
| 2234 |
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|
| 2235 |
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|
| 2236 |
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| 2237 |
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|
| 2238 |
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|
| 2239 |
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|
| 2240 |
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|
| 2241 |
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|
| 2242 |
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|
| 2243 |
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|
| 2244 |
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|
| 2245 |
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|
| 2246 |
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|
| 2247 |
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|
| 2248 |
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|
| 2249 |
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|
| 2250 |
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|
| 2251 |
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|
| 2252 |
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|
| 2253 |
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|
| 2254 |
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|
| 2255 |
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|
| 2256 |
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|
| 2257 |
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|
| 2258 |
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|
| 2259 |
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|
| 2260 |
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|
| 2261 |
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|
| 2262 |
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|
| 2263 |
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| 2264 |
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| 2265 |
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| 2266 |
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| 2267 |
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| 2268 |
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| 2269 |
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| 2270 |
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|
| 2271 |
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|
| 2272 |
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|
| 2273 |
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|
| 2274 |
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|
| 2275 |
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|
| 2276 |
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"##uis": 2458,
|
| 2653 |
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"louis": 2459,
|
| 2654 |
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"##leans": 2460,
|
| 2655 |
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"##ikpapan": 2461,
|
| 2656 |
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"100": 2462,
|
| 2657 |
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"106": 2463,
|
| 2658 |
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"10h30": 2464,
|
| 2659 |
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"10h1": 2465,
|
| 2660 |
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"10h24": 2466,
|
| 2661 |
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"1055": 2467,
|
| 2662 |
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"10h20": 2468,
|
| 2663 |
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"1059": 2469,
|
| 2664 |
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"1083": 2470,
|
| 2665 |
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"12h": 2471,
|
| 2666 |
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"121": 2472,
|
| 2667 |
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"12h30": 2473,
|
| 2668 |
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"1209": 2474,
|
| 2669 |
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"1207": 2475,
|
| 2670 |
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"hoà_bình": 2476,
|
| 2671 |
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"xin_lỗi": 2477,
|
| 2672 |
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"trung_chuyển": 2478,
|
| 2673 |
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"##_diện": 2479,
|
| 2674 |
+
"##iss": 2480,
|
| 2675 |
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"mười_ba": 2481,
|
| 2676 |
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"##icino": 2482,
|
| 2677 |
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"quảng_cáo": 2483,
|
| 2678 |
+
"11h30": 2484,
|
| 2679 |
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"11h5": 2485,
|
| 2680 |
+
"##adford": 2486,
|
| 2681 |
+
"luton": 2487,
|
| 2682 |
+
"luck": 2488,
|
| 2683 |
+
"lucer": 2489,
|
| 2684 |
+
"##ng_cái": 2490,
|
| 2685 |
+
"##aplet": 2491,
|
| 2686 |
+
"bourne": 2492,
|
| 2687 |
+
"kuching": 2493,
|
| 2688 |
+
"##stern": 2494,
|
| 2689 |
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"pari": 2495,
|
| 2690 |
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"sevil": 2496,
|
| 2691 |
+
"279": 2497,
|
| 2692 |
+
"thuộc": 2498,
|
| 2693 |
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"2022": 2499,
|
| 2694 |
+
"ap58": 2500,
|
| 2695 |
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"ap68": 2501,
|
| 2696 |
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"##taa": 2502,
|
| 2697 |
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"trống_không": 2503,
|
| 2698 |
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"dubro": 2504,
|
| 2699 |
+
"okay": 2505,
|
| 2700 |
+
"3084": 2506,
|
| 2701 |
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"doh": 2507,
|
| 2702 |
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"##ng_qu": 2508,
|
| 2703 |
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"tương_tự": 2509,
|
| 2704 |
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"210": 2510,
|
| 2705 |
+
"2153": 2511,
|
| 2706 |
+
"lazio": 2512,
|
| 2707 |
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"##usetts": 2513,
|
| 2708 |
+
"atl": 2514,
|
| 2709 |
+
"cái": 2515,
|
| 2710 |
+
"a330": 2516,
|
| 2711 |
+
"a350": 2517,
|
| 2712 |
+
"làm_sao": 2518,
|
| 2713 |
+
"##enham": 2519,
|
| 2714 |
+
"tam_đảo": 2520,
|
| 2715 |
+
"tên_riêng": 2521,
|
| 2716 |
+
"19h4": 2522,
|
| 2717 |
+
"312": 2523,
|
| 2718 |
+
"315": 2524,
|
| 2719 |
+
"311": 2525,
|
| 2720 |
+
"##nabh": 2526,
|
| 2721 |
+
"coast": 2527,
|
| 2722 |
+
"inc": 2528,
|
| 2723 |
+
"india": 2529,
|
| 2724 |
+
"459": 2530,
|
| 2725 |
+
"734": 2531,
|
| 2726 |
+
"##eda": 2532,
|
| 2727 |
+
"##eds": 2533,
|
| 2728 |
+
"##edab": 2534,
|
| 2729 |
+
"america": 2535,
|
| 2730 |
+
"art": 2536,
|
| 2731 |
+
"colombo": 2537,
|
| 2732 |
+
"đài_loan": 2538,
|
| 2733 |
+
"##emen": 2539,
|
| 2734 |
+
"stansted": 2540,
|
| 2735 |
+
"staplet": 2541,
|
| 2736 |
+
"tây_ninh": 2542,
|
| 2737 |
+
"vantaa": 2543,
|
| 2738 |
+
"##nty": 2544,
|
| 2739 |
+
"##caster": 2545,
|
| 2740 |
+
"1409": 2546,
|
| 2741 |
+
"7h55": 2547,
|
| 2742 |
+
"7h05": 2548,
|
| 2743 |
+
"72s": 2549,
|
| 2744 |
+
"aero": 2550,
|
| 2745 |
+
"be1": 2551,
|
| 2746 |
+
"beij": 2552,
|
| 2747 |
+
"beirut": 2553,
|
| 2748 |
+
"bradford": 2554,
|
| 2749 |
+
"bremen": 2555,
|
| 2750 |
+
"naypyid": 2556,
|
| 2751 |
+
"norfol": 2557,
|
| 2752 |
+
"tpa": 2558,
|
| 2753 |
+
"vũng_tàu": 2559,
|
| 2754 |
+
"##arson": 2560,
|
| 2755 |
+
"##arszawa": 2561,
|
| 2756 |
+
"##arkand": 2562,
|
| 2757 |
+
"139": 2563,
|
| 2758 |
+
"1373": 2564,
|
| 2759 |
+
"1384": 2565,
|
| 2760 |
+
"229": 2566,
|
| 2761 |
+
"2211": 2567,
|
| 2762 |
+
"297": 2568,
|
| 2763 |
+
"345": 2569,
|
| 2764 |
+
"cardif": 2570,
|
| 2765 |
+
"cathay": 2571,
|
| 2766 |
+
"hamad": 2572,
|
| 2767 |
+
"ninh_bình": 2573,
|
| 2768 |
+
"orleans": 2574,
|
| 2769 |
+
"##_nguyên": 2575,
|
| 2770 |
+
"##see": 2576,
|
| 2771 |
+
"##yazak": 2577,
|
| 2772 |
+
"balikpapan": 2578,
|
| 2773 |
+
"portofino": 2579,
|
| 2774 |
+
"salt_lake": 2580,
|
| 2775 |
+
"18h5": 2581,
|
| 2776 |
+
"ea_là": 2582,
|
| 2777 |
+
"eastern": 2583,
|
| 2778 |
+
"haneda": 2584,
|
| 2779 |
+
"miyazak": 2585,
|
| 2780 |
+
"minnes": 2586,
|
| 2781 |
+
"minsk": 2587,
|
| 2782 |
+
"tiếp_tục": 2588,
|
| 2783 |
+
"xung_qu": 2589,
|
| 2784 |
+
"##shpool": 2590,
|
| 2785 |
+
"##torua": 2591,
|
| 2786 |
+
"indiana": 2592,
|
| 2787 |
+
"7375": 2593,
|
| 2788 |
+
"cao_lãnh": 2594,
|
| 2789 |
+
"hay_là": 2595,
|
| 2790 |
+
"lester": 2596,
|
| 2791 |
+
"leeds": 2597,
|
| 2792 |
+
"##umi": 2598,
|
| 2793 |
+
"##umicino": 2599,
|
| 2794 |
+
"burma": 2600,
|
| 2795 |
+
"##andu": 2601,
|
| 2796 |
+
"mainz": 2602,
|
| 2797 |
+
"382": 2603,
|
| 2798 |
+
"746": 2604,
|
| 2799 |
+
"kolkata": 2605,
|
| 2800 |
+
"midway": 2606,
|
| 2801 |
+
"tàu_bay": 2607,
|
| 2802 |
+
"đại_liên": 2608,
|
| 2803 |
+
"đại_diện": 2609,
|
| 2804 |
+
"bari": 2610,
|
| 2805 |
+
"2704": 2611,
|
| 2806 |
+
"3357": 2612,
|
| 2807 |
+
"402": 2613,
|
| 2808 |
+
"405": 2614,
|
| 2809 |
+
"lhr": 2615,
|
| 2810 |
+
"not": 2616,
|
| 2811 |
+
"novos": 2617,
|
| 2812 |
+
"perugia": 2618,
|
| 2813 |
+
"peretol": 2619,
|
| 2814 |
+
"samui": 2620,
|
| 2815 |
+
"samarkand": 2621,
|
| 2816 |
+
"southwest": 2622,
|
| 2817 |
+
"southampton": 2623,
|
| 2818 |
+
"vì_vậy": 2624,
|
| 2819 |
+
"westchester": 2625,
|
| 2820 |
+
"##nessee": 2626,
|
| 2821 |
+
"##hmandu": 2627,
|
| 2822 |
+
"thích_hợp": 2628,
|
| 2823 |
+
"##alilei": 2629,
|
| 2824 |
+
"21h34": 2630,
|
| 2825 |
+
"2170": 2631,
|
| 2826 |
+
"brasov": 2632,
|
| 2827 |
+
"brasília": 2633,
|
| 2828 |
+
"2h25": 2634,
|
| 2829 |
+
"323": 2635,
|
| 2830 |
+
"4977": 2636,
|
| 2831 |
+
"4400": 2637,
|
| 2832 |
+
"632": 2638,
|
| 2833 |
+
"608": 2639,
|
| 2834 |
+
"798": 2640,
|
| 2835 |
+
"771": 2641,
|
| 2836 |
+
"8415": 2642,
|
| 2837 |
+
"8h16": 2643,
|
| 2838 |
+
"alaska": 2644,
|
| 2839 |
+
"ahmedab": 2645,
|
| 2840 |
+
"bwi": 2646,
|
| 2841 |
+
"biên_hoà": 2647,
|
| 2842 |
+
"b777": 2648,
|
| 2843 |
+
"cạnh": 2649,
|
| 2844 |
+
"cebu": 2650,
|
| 2845 |
+
"cvg": 2651,
|
| 2846 |
+
"cước": 2652,
|
| 2847 |
+
"cẩm_ph": 2653,
|
| 2848 |
+
"county": 2654,
|
| 2849 |
+
"dự_kiến": 2655,
|
| 2850 |
+
"düsseldorf": 2656,
|
| 2851 |
+
"doncaster": 2657,
|
| 2852 |
+
"emirat": 2658,
|
| 2853 |
+
"fiumicino": 2659,
|
| 2854 |
+
"frédé": 2660,
|
| 2855 |
+
"finnai": 2661,
|
| 2856 |
+
"guardia": 2662,
|
| 2857 |
+
"gold": 2663,
|
| 2858 |
+
"goreme": 2664,
|
| 2859 |
+
"galileo_g": 2665,
|
| 2860 |
+
"hy_vọng": 2666,
|
| 2861 |
+
"hà_tiên": 2667,
|
| 2862 |
+
"hawaii": 2668,
|
| 2863 |
+
"hartfield": 2669,
|
| 2864 |
+
"illinois": 2670,
|
| 2865 |
+
"ipoh": 2671,
|
| 2866 |
+
"jfk": 2672,
|
| 2867 |
+
"ký_hiệu": 2673,
|
| 2868 |
+
"kalamata": 2674,
|
| 2869 |
+
"kịch_bản": 2675,
|
| 2870 |
+
"kathmandu": 2676,
|
| 2871 |
+
"kemi": 2677,
|
| 2872 |
+
"kansas": 2678,
|
| 2873 |
+
"lệ_giang": 2679,
|
| 2874 |
+
"lặp": 2680,
|
| 2875 |
+
"long_thành": 2681,
|
| 2876 |
+
"lan_châu": 2682,
|
| 2877 |
+
"lạng_sơn": 2683,
|
| 2878 |
+
"mci": 2684,
|
| 2879 |
+
"móng_cái": 2685,
|
| 2880 |
+
"mùa": 2686,
|
| 2881 |
+
"múi_giờ": 2687,
|
| 2882 |
+
"massach": 2688,
|
| 2883 |
+
"mashad": 2689,
|
| 2884 |
+
"mặt_đất": 2690,
|
| 2885 |
+
"matsuy": 2691,
|
| 2886 |
+
"medan": 2692,
|
| 2887 |
+
"nancy": 2693,
|
| 2888 |
+
"nairobi": 2694,
|
| 2889 |
+
"olso": 2695,
|
| 2890 |
+
"pearson": 2696,
|
| 2891 |
+
"patna": 2697,
|
| 2892 |
+
"padang": 2698,
|
| 2893 |
+
"rotorua": 2699,
|
| 2894 |
+
"rimini": 2700,
|
| 2895 |
+
"suvar": 2701,
|
| 2896 |
+
"shef": 2702,
|
| 2897 |
+
"sfo": 2703,
|
| 2898 |
+
"swiss": 2704,
|
| 2899 |
+
"sầm_sơn": 2705,
|
| 2900 |
+
"tbb": 2706,
|
| 2901 |
+
"tdh": 2707,
|
| 2902 |
+
"tennessee": 2708,
|
| 2903 |
+
"toscana": 2709,
|
| 2904 |
+
"tottenham": 2710,
|
| 2905 |
+
"tower": 2711,
|
| 2906 |
+
"utah": 2712,
|
| 2907 |
+
"václav": 2713,
|
| 2908 |
+
"varanas": 2714,
|
| 2909 |
+
"vị_trí": 2715,
|
| 2910 |
+
"welshpool": 2716,
|
| 2911 |
+
"xuống": 2717,
|
| 2912 |
+
"yyz": 2718,
|
| 2913 |
+
"yangon": 2719,
|
| 2914 |
+
"yokoham": 2720,
|
| 2915 |
+
"áp_dụng": 2721,
|
| 2916 |
+
"ô_tô": 2722,
|
| 2917 |
+
"ôn_châu": 2723,
|
| 2918 |
+
"đều": 2724,
|
| 2919 |
+
"##ibirsk": 2725,
|
| 2920 |
+
"##ric_chop": 2726,
|
| 2921 |
+
"china": 2727,
|
| 2922 |
+
"chắc_chắn": 2728,
|
| 2923 |
+
"bagdogra": 2729,
|
| 2924 |
+
"thiết_bị": 2730,
|
| 2925 |
+
"thay_vì": 2731,
|
| 2926 |
+
"thái_nguyên": 2732,
|
| 2927 |
+
"thị_trấn": 2733,
|
| 2928 |
+
"thừa_thiên_huế": 2734,
|
| 2929 |
+
"pheonix": 2735,
|
| 2930 |
+
"bucharest": 2736,
|
| 2931 |
+
"hồng_kiều": 2737,
|
| 2932 |
+
"giờ_địa_phương": 2738,
|
| 2933 |
+
"##in_warszawa": 2739,
|
| 2934 |
+
"tối_đa": 2740,
|
| 2935 |
+
"cambridge": 2741,
|
| 2936 |
+
"washingtion": 2742,
|
| 2937 |
+
"10h17": 2743,
|
| 2938 |
+
"10h20p": 2744,
|
| 2939 |
+
"11h58": 2745,
|
| 2940 |
+
"lucknow": 2746,
|
| 2941 |
+
"lucerne": 2747,
|
| 2942 |
+
"bournemouth": 2748,
|
| 2943 |
+
"sevilla": 2749,
|
| 2944 |
+
"dubrovnik": 2750,
|
| 2945 |
+
"19h40": 2751,
|
| 2946 |
+
"##nabhumi": 2752,
|
| 2947 |
+
"stapleton": 2753,
|
| 2948 |
+
"beijing": 2754,
|
| 2949 |
+
"naypyidaw": 2755,
|
| 2950 |
+
"norfolk": 2756,
|
| 2951 |
+
"137338": 2757,
|
| 2952 |
+
"cardiff": 2758,
|
| 2953 |
+
"18h50": 2759,
|
| 2954 |
+
"miyazaki": 2760,
|
| 2955 |
+
"minnesota": 2761,
|
| 2956 |
+
"xung_quanh": 2762,
|
| 2957 |
+
"novosibirsk": 2763,
|
| 2958 |
+
"peretola": 2764,
|
| 2959 |
+
"ahmedabad": 2765,
|
| 2960 |
+
"cẩm_phả": 2766,
|
| 2961 |
+
"emirates": 2767,
|
| 2962 |
+
"frédéric_chop": 2768,
|
| 2963 |
+
"finnair": 2769,
|
| 2964 |
+
"galileo_galilei": 2770,
|
| 2965 |
+
"massachusetts": 2771,
|
| 2966 |
+
"matsuyama": 2772,
|
| 2967 |
+
"suvarnabhumi": 2773,
|
| 2968 |
+
"sheffield": 2774,
|
| 2969 |
+
"václav_havel": 2775,
|
| 2970 |
+
"varanasi": 2776,
|
| 2971 |
+
"yokohama": 2777,
|
| 2972 |
+
"frédéric_chopin_warszawa": 2778
|
| 2973 |
+
}
|
| 2974 |
+
}
|
| 2975 |
+
}
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,54 @@
|
|
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|
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|
|
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|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "<s>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"1": {
|
| 12 |
+
"content": "</s>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"2": {
|
| 20 |
+
"content": "<unk>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"3": {
|
| 28 |
+
"content": "<pad>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"4": {
|
| 36 |
+
"content": "<mask>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"bos_token": "<s>",
|
| 45 |
+
"clean_up_tokenization_spaces": true,
|
| 46 |
+
"cls_token": "<s>",
|
| 47 |
+
"eos_token": "</s>",
|
| 48 |
+
"mask_token": "<mask>",
|
| 49 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 50 |
+
"pad_token": "</s>",
|
| 51 |
+
"sep_token": "</s>",
|
| 52 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
| 53 |
+
"unk_token": "<unk>"
|
| 54 |
+
}
|