Add new SentenceTransformer model.
Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +7 -0
- 2_Dense/config.json +1 -0
- 2_Dense/pytorch_model.bin +3 -0
- 3_Dense/config.json +1 -0
- 3_Dense/pytorch_model.bin +3 -0
- README.md +57 -0
- added_tokens.json +3 -0
- bpe.codes +0 -0
- config.json +30 -0
- config_sentence_transformers.json +7 -0
- model.safetensors +3 -0
- modules.json +26 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +9 -0
- tokenizer_config.json +54 -0
- vocab.txt +0 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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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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2_Dense/config.json
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{"in_features": 1, "out_features": 1, "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 sha256:49598f000e4c9384fdc3f5202380ac7d5d9fdfc71a64f5702607688ee69ce9eb
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size 1596
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3_Dense/config.json
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{"in_features": 1, "out_features": 1, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}
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3_Dense/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:447fef4086003ae614a0dcc7937934670b50df5a927d38ee94157a0b89ca955a
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size 1596
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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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---
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# tmnam20/test-model1
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1 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('tmnam20/test-model1')
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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=tmnam20/test-model1)
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## Full Model Architecture
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```
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MultiTaskSentenceTransformer(
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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(task_task1_head): Dense({'in_features': 1, 'out_features': 1, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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(task_task2_head): Dense({'in_features': 1, 'out_features': 1, '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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added_tokens.json
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{
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"<mask>": 64000
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}
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bpe.codes
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config.json
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{
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"_name_or_path": "demdecuong/vihealthbert-base-syllable",
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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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"finetuning_task": "word-level",
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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-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": "4.37.2",
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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": "4.37.2",
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"pytorch": "2.1.1+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 sha256:09a1c94f8d68ee037ee0270ce34d5a0360d601d8a04ce3d28742f3e185005ee5
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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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"idx": 2,
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"name": "task_task1_head",
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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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"idx": 3,
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"name": "task_task2_head",
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"path": "3_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": 128,
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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": "<s>",
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"unk_token": "<unk>"
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}
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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": "<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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"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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"2": {
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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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"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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"64000": {
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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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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"tokenizer_class": "PhobertTokenizer",
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"unk_token": "<unk>"
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}
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vocab.txt
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