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Parent(s):
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Browse files- 1_Pooling/config.json +9 -0
- 2_Dense/config.json +1 -0
- 2_Dense/pytorch_model.bin +3 -0
- README.md +89 -0
- config.json +27 -0
- config_sentence_transformers.json +7 -0
- eval/similarity_evaluation_results.csv +11 -0
- model.safetensors +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -0
- vocab.txt +0 -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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2_Dense/config.json
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{"in_features": 768, "out_features": 256, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}
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2_Dense/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3780cba8c2f3fd9aa905048bfd27434dd875dd730503a99a5c1fe4efcc6e1039
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size 789116
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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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---
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# {MODEL_NAME}
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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('{MODEL_NAME}')
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embeddings = model.encode(sentences)
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print(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={MODEL_NAME})
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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 98 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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`sentence_transformers.losses.CosineSimilarityLoss.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": 1000,
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"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
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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": 9e-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": 700,
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"weight_decay": 9.5e-07
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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: BertModel
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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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(2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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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": "jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_10_Epochs",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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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-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.37.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 31002
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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": "2.2.0",
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"transformers": "4.16.2",
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"pytorch": "1.10.0+cu111"
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}
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}
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eval/similarity_evaluation_results.csv
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epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
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0,-1,0.7301747325018917,0.6899959302306742,0.7040307131973579,0.6694706766829714,0.7024239343845635,0.6666988267314748,0.6697377604578035,0.6540034476718587
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1,-1,0.720282231893454,0.6630584492103305,0.6862280786517337,0.6450775220190524,0.6837285652663181,0.6470464762071716,0.6378151033969421,0.6214238190391161
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2,-1,0.726880232528087,0.6877578856368456,0.6824428502056339,0.6440186622112196,0.6784442455689068,0.6432989000691184,0.6945672269331964,0.6783112809876257
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3,-1,0.7211863925137892,0.6812362718204202,0.704965803885943,0.6751718929210138,0.7047718037099641,0.673025732855964,0.6581695358192801,0.6274203784053763
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4,-1,0.7178549698887915,0.6918861262512684,0.6834438261340964,0.6635463123036088,0.6891292658083976,0.6696281930180209,0.6838097529918581,0.6582913923482069
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5,-1,0.7153448999395546,0.6750034379515858,0.6941137176589776,0.6484903759451255,0.6933302251587777,0.6456572696411098,0.69298893813314,0.6576394497392518
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6,-1,0.7099304137021265,0.6750318784009698,0.711899010056462,0.6892149117360538,0.7169945789537423,0.6988255959009501,0.6616784835985352,0.6411199241009333
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7,-1,0.6606382268495986,0.6223689170494133,0.6802425762913461,0.6320102293905693,0.6805337090263458,0.6303541201456737,0.6179251280305234,0.5957508441529199
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8,-1,0.6987852597696748,0.6584357823220022,0.6929621028318754,0.6581119987444004,0.6870061808243686,0.6448040561595915,0.6784163877796915,0.6269434539464764
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9,-1,0.7202625174178684,0.6864890040489466,0.7114650158770163,0.6940651022194535,0.7106765084247617,0.6892039731016752,0.6863313983010619,0.6484553723151144
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5b81a2fab6fee0785706ea9e6abacc92314221e1eb25ffec753a1b9b68fe901f
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size 439425888
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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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"idx": 2,
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"name": "2",
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"path": "2_Dense",
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"type": "sentence_transformers.models.Dense"
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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": 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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"cls_token": {
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"content": "[CLS]",
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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": "[PAD]",
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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": "[SEP]",
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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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"content": "[UNK]",
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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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}
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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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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"special": true
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},
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"1": {
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"content": "[PAD]",
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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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"special": true
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},
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"3": {
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"content": "[UNK]",
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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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"special": true
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},
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"4": {
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"content": "[CLS]",
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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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"special": true
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},
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"5": {
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"content": "[SEP]",
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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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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"max_length": 256,
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"model_max_length": 512,
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"never_split": null,
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"pad_to_multiple_of": null,
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"pad_token": "[PAD]",
|
54 |
+
"pad_token_type_id": 0,
|
55 |
+
"padding_side": "right",
|
56 |
+
"sep_token": "[SEP]",
|
57 |
+
"stride": 0,
|
58 |
+
"strip_accents": false,
|
59 |
+
"tokenize_chinese_chars": true,
|
60 |
+
"tokenizer_class": "BertTokenizer",
|
61 |
+
"truncation_side": "right",
|
62 |
+
"truncation_strategy": "longest_first",
|
63 |
+
"unk_token": "[UNK]"
|
64 |
+
}
|
vocab.txt
ADDED
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|
|