Sentence Similarity
sentence-transformers
Safetensors
German
bert
linktransformer
tabular-classification
text-embeddings-inference
Instructions to use dell-research-harvard/lt-wikidata-comp-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dell-research-harvard/lt-wikidata-comp-de with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dell-research-harvard/lt-wikidata-comp-de") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit ·
0659376
1
Parent(s): c4ea972
Modified validation and training for linktransformer model
Browse files- .gitattributes +2 -0
- 1_Pooling/config.json +3 -1
- LT_training_config.json +5 -2
- README.md +17 -7
- config.json +2 -2
- config_sentence_transformers.json +3 -3
- model.safetensors +3 -0
- special_tokens_map.json +35 -5
- tokenizer_config.json +49 -0
.gitattributes
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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.git/lfs/objects/e4/61/e4618580bff28fbdf2e9114acbe403a6c5165f2bb60546eae35a01d385254ded filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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.git/lfs/objects/e4/61/e4618580bff28fbdf2e9114acbe403a6c5165f2bb60546eae35a01d385254ded filter=lfs diff=lfs merge=lfs -text
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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.git/lfs/objects/9f/95/9f95b264b70d80ae32ed9479775e4ec50212969640b262be509ffc9736a58def filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json
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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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}
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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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LT_training_config.json
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"train_batch_size": 64,
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"num_epochs": 100,
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"warm_up_perc": 1,
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"learning_rate": 2e-
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"val_perc": 0.2,
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"wandb_names": {
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"project": "linkage",
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},
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"add_pooling_layer": false,
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"large_val": true,
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"eval_steps_perc": 0.
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"test_at_end": true,
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"save_val_test_pickles": true,
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"eval_type": "retrieval",
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"training_dataset": "dataframe",
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"base_model_path": "Sahajtomar/German-semantic",
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"train_batch_size": 64,
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"num_epochs": 100,
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"warm_up_perc": 1,
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"learning_rate": 2e-05,
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"loss_type": "supcon",
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"val_perc": 0.2,
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"wandb_names": {
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"project": "linkage",
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},
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"add_pooling_layer": false,
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"large_val": true,
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"eval_steps_perc": 0.5,
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"test_at_end": true,
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"save_val_test_pickles": true,
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"val_query_prop": 0.5,
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"loss_params": {},
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"eval_type": "retrieval",
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"training_dataset": "dataframe",
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"base_model_path": "Sahajtomar/German-semantic",
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README.md
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# dell-research-harvard/lt-wikidata-comp-de
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This is a [LinkTransformer](https://github.
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It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more.
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Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well.
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It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length
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```
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{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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```
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{
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"epochs": 100,
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"evaluation_steps":
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
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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": 2e-
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps":
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"weight_decay": 0.01
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}
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```
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LinkTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 1024, '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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-
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# dell-research-harvard/lt-wikidata-comp-de
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This is a [LinkTransformer](https://linktransformer.github.io/) model. At its core this model this is a sentence transformer model [sentence-transformers](https://www.SBERT.net) model- it just wraps around the class.
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It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more.
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Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well.
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It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 667 with parameters:
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```
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{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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```
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{
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"epochs": 100,
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"evaluation_steps": 334,
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
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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": 2e-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": 66700,
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"weight_decay": 0.01
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}
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```
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LinkTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 1024, '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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```
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@misc{arora2023linktransformer,
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title={LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models},
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author={Abhishek Arora and Melissa Dell},
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year={2023},
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eprint={2309.00789},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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config.json
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{
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"_name_or_path": "models/linkage_de_aliases
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"architectures": [
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"BertModel"
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],
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 31102
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{
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"_name_or_path": "models/linkage_de_aliases",
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"architectures": [
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"BertModel"
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],
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.35.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 31102
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.
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"transformers": "4.
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"pytorch": "2.
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}
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{
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"__version__": {
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"sentence_transformers": "2.3.1",
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"transformers": "4.35.1",
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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:f631da819feeb5679e8f047c1d7377b39545cae598ac3aca2708a468efcdcdec
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size 1342988112
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special_tokens_map.json
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}
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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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"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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tokenizer_config.json
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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": false,
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"mask_token": "[MASK]",
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"max_len": 512,
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"model_max_length": 512,
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"sep_token": "[SEP]",
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"strip_accents": false,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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{
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"special": true
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"101": {
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"special": true
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},
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"102": {
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"special": true
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},
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"103": {
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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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"104": {
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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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},
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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": false,
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"mask_token": "[MASK]",
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"max_len": 512,
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"max_length": 512,
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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]",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"sep_token": "[SEP]",
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"stride": 0,
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"strip_accents": false,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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}
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