Add new SentenceTransformer model.
Browse files- 1_Pooling/config.json +9 -0
- README.md +127 -0
- config.json +27 -0
- config_sentence_transformers.json +7 -0
- modules.json +14 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +112 -0
- tokenizer.json +0 -0
- tokenizer_config.json +957 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 2048,
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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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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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# Wissam42/sentence-croissant-llm-base
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 2048 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('Wissam42/sentence-croissant-llm-base')
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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('Wissam42/sentence-croissant-llm-base')
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model = AutoModel.from_pretrained('Wissam42/sentence-croissant-llm-base')
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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=Wissam42/sentence-croissant-llm-base)
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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 360 with parameters:
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```
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{'batch_size': 16, '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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`__main__.CosineSimilarityLoss`
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Parameters of the fit()-Method:
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```
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{
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"epochs": 10,
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"evaluation_steps": 500,
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"evaluator": "__main__.CustomEmbeddingSimilarityEvaluator",
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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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"eps": 1e-06,
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"lr": 1e-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": 360,
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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': 256, 'do_lower_case': False}) with Transformer model: LlamaModel
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(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': 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": "/dbfs/FileStore/test_training/wissam/training_stsbenchmark_croissantllm-CroissantLLMBase-2024-02-06_10-53-25/",
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"architectures": [
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"LlamaModel"
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],
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"attention_bias": false,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5504,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.34.0",
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"use_cache": true,
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"vocab_size": 32000
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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": "4.34.0",
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"pytorch": "2.0.1+cpu"
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}
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}
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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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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:b736e2d87d3fb505d5493c69f926d7274b916ebb5297f714d6dd3a4fa43ce56e
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size 5119618821
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sentence_bert_config.json
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{
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"max_seq_length": 256,
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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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"additional_special_tokens": [
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"<unk>",
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"<s>",
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"</s>",
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"<pad>",
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"<extra_id_0>",
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"<extra_id_1>",
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"<extra_id_2>",
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"<extra_id_3>",
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"<extra_id_4>",
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"<extra_id_5>",
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"<extra_id_6>",
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"<extra_id_7>",
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"<extra_id_8>",
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"<extra_id_9>",
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"<extra_id_10>",
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"<extra_id_11>",
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"<extra_id_12>",
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"<extra_id_13>",
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"<extra_id_14>",
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"<extra_id_15>",
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"<extra_id_16>",
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"<extra_id_17>",
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"<extra_id_18>",
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"<extra_id_19>",
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"<extra_id_20>",
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"<extra_id_21>",
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"<extra_id_22>",
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"<extra_id_23>",
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"<extra_id_24>",
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"<extra_id_25>",
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"<extra_id_26>",
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"<extra_id_27>",
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"<extra_id_28>",
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"<extra_id_29>",
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"<extra_id_30>",
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"<extra_id_31>",
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"<extra_id_32>",
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"<extra_id_33>",
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"<extra_id_34>",
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"<extra_id_35>",
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"<extra_id_36>",
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"<extra_id_37>",
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"<extra_id_38>",
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"<extra_id_39>",
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"<extra_id_40>",
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"<extra_id_41>",
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"<extra_id_42>",
|
| 50 |
+
"<extra_id_43>",
|
| 51 |
+
"<extra_id_44>",
|
| 52 |
+
"<extra_id_45>",
|
| 53 |
+
"<extra_id_46>",
|
| 54 |
+
"<extra_id_47>",
|
| 55 |
+
"<extra_id_48>",
|
| 56 |
+
"<extra_id_49>",
|
| 57 |
+
"<extra_id_50>",
|
| 58 |
+
"<extra_id_51>",
|
| 59 |
+
"<extra_id_52>",
|
| 60 |
+
"<extra_id_53>",
|
| 61 |
+
"<extra_id_54>",
|
| 62 |
+
"<extra_id_55>",
|
| 63 |
+
"<extra_id_56>",
|
| 64 |
+
"<extra_id_57>",
|
| 65 |
+
"<extra_id_58>",
|
| 66 |
+
"<extra_id_59>",
|
| 67 |
+
"<extra_id_60>",
|
| 68 |
+
"<extra_id_61>",
|
| 69 |
+
"<extra_id_62>",
|
| 70 |
+
"<extra_id_63>",
|
| 71 |
+
"<extra_id_64>",
|
| 72 |
+
"<extra_id_65>",
|
| 73 |
+
"<extra_id_66>",
|
| 74 |
+
"<extra_id_67>",
|
| 75 |
+
"<extra_id_68>",
|
| 76 |
+
"<extra_id_69>",
|
| 77 |
+
"<extra_id_70>",
|
| 78 |
+
"<extra_id_71>",
|
| 79 |
+
"<extra_id_72>",
|
| 80 |
+
"<extra_id_73>",
|
| 81 |
+
"<extra_id_74>",
|
| 82 |
+
"<extra_id_75>",
|
| 83 |
+
"<extra_id_76>",
|
| 84 |
+
"<extra_id_77>",
|
| 85 |
+
"<extra_id_78>",
|
| 86 |
+
"<extra_id_79>",
|
| 87 |
+
"<extra_id_80>",
|
| 88 |
+
"<extra_id_81>",
|
| 89 |
+
"<extra_id_82>",
|
| 90 |
+
"<extra_id_83>",
|
| 91 |
+
"<extra_id_84>",
|
| 92 |
+
"<extra_id_85>",
|
| 93 |
+
"<extra_id_86>",
|
| 94 |
+
"<extra_id_87>",
|
| 95 |
+
"<extra_id_88>",
|
| 96 |
+
"<extra_id_89>",
|
| 97 |
+
"<extra_id_90>",
|
| 98 |
+
"<extra_id_91>",
|
| 99 |
+
"<extra_id_92>",
|
| 100 |
+
"<extra_id_93>",
|
| 101 |
+
"<extra_id_94>",
|
| 102 |
+
"<extra_id_95>",
|
| 103 |
+
"<extra_id_96>",
|
| 104 |
+
"<extra_id_97>",
|
| 105 |
+
"<extra_id_98>",
|
| 106 |
+
"<extra_id_99>"
|
| 107 |
+
],
|
| 108 |
+
"bos_token": "<s>",
|
| 109 |
+
"eos_token": "</s>",
|
| 110 |
+
"pad_token": "<pad>",
|
| 111 |
+
"unk_token": "<unk>"
|
| 112 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,957 @@
|
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| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "<unk>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"1": {
|
| 12 |
+
"content": "<s>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
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"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
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|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
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"2": {
|
| 20 |
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"content": "</s>",
|
| 21 |
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"lstrip": false,
|
| 22 |
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"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
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|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
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"3": {
|
| 28 |
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"content": "<pad>",
|
| 29 |
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|
| 30 |
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|
| 31 |
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"rstrip": false,
|
| 32 |
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|
| 33 |
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|
| 34 |
+
},
|
| 35 |
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"4": {
|
| 36 |
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"content": "<extra_id_0>",
|
| 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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"5": {
|
| 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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"6": {
|
| 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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"7": {
|
| 60 |
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|
| 61 |
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| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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| 75 |
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"9": {
|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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| 91 |
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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"12": {
|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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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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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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|
| 195 |
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"24": {
|
| 196 |
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|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
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|
| 202 |
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},
|
| 203 |
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"25": {
|
| 204 |
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|
| 205 |
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|
| 206 |
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|
| 207 |
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|
| 208 |
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|
| 209 |
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|
| 210 |
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|
| 211 |
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|
| 212 |
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|
| 213 |
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|
| 214 |
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|
| 215 |
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|
| 216 |
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|
| 217 |
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|
| 218 |
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|
| 219 |
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|
| 220 |
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|
| 221 |
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|
| 222 |
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|
| 223 |
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|
| 224 |
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|
| 225 |
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|
| 226 |
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|
| 227 |
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|
| 228 |
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|
| 229 |
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|
| 230 |
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|
| 231 |
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|
| 232 |
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|
| 233 |
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|
| 234 |
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| 235 |
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|
| 236 |
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|
| 237 |
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|
| 238 |
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|
| 239 |
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|
| 240 |
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|
| 241 |
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|
| 242 |
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|
| 243 |
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|
| 244 |
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|
| 245 |
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|
| 246 |
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|
| 247 |
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|
| 248 |
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|
| 249 |
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|
| 250 |
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|
| 251 |
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|
| 252 |
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|
| 253 |
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|
| 254 |
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|
| 255 |
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|
| 256 |
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|
| 257 |
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|
| 258 |
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|
| 259 |
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|
| 260 |
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|
| 261 |
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|
| 262 |
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| 263 |
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|
| 264 |
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|
| 265 |
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|
| 266 |
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|
| 267 |
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|
| 268 |
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|
| 269 |
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|
| 270 |
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|
| 271 |
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|
| 272 |
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|
| 273 |
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|
| 274 |
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|
| 275 |
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|
| 276 |
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|
| 277 |
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|
| 278 |
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|
| 279 |
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|
| 280 |
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|
| 281 |
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|
| 282 |
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|
| 283 |
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|
| 284 |
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|
| 285 |
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|
| 286 |
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|
| 287 |
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|
| 288 |
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|
| 289 |
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|
| 290 |
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|
| 291 |
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|
| 292 |
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|
| 293 |
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|
| 294 |
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|
| 295 |
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|
| 296 |
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|
| 297 |
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|
| 298 |
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|
| 299 |
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|
| 300 |
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|
| 301 |
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|
| 302 |
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|
| 303 |
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|
| 304 |
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|
| 305 |
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|
| 306 |
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|
| 307 |
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|
| 308 |
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|
| 309 |
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|
| 310 |
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|
| 311 |
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|
| 312 |
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|
| 313 |
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|
| 314 |
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|
| 315 |
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|
| 316 |
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|
| 317 |
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|
| 318 |
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|
| 319 |
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|
| 320 |
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|
| 321 |
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|
| 322 |
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|
| 323 |
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|
| 324 |
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|
| 325 |
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|
| 326 |
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|
| 327 |
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|
| 328 |
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|
| 329 |
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|
| 330 |
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|
| 331 |
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|
| 332 |
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|
| 333 |
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|
| 334 |
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|
| 335 |
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|
| 336 |
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|
| 337 |
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|
| 338 |
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|
| 339 |
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|
| 340 |
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|
| 341 |
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|
| 342 |
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|
| 343 |
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| 344 |
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}
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