Instructions to use danangwijaya/IndoRetNet-Liputan6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danangwijaya/IndoRetNet-Liputan6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="danangwijaya/IndoRetNet-Liputan6")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("danangwijaya/IndoRetNet-Liputan6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use danangwijaya/IndoRetNet-Liputan6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "danangwijaya/IndoRetNet-Liputan6" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "danangwijaya/IndoRetNet-Liputan6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/danangwijaya/IndoRetNet-Liputan6
- SGLang
How to use danangwijaya/IndoRetNet-Liputan6 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "danangwijaya/IndoRetNet-Liputan6" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "danangwijaya/IndoRetNet-Liputan6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "danangwijaya/IndoRetNet-Liputan6" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "danangwijaya/IndoRetNet-Liputan6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use danangwijaya/IndoRetNet-Liputan6 with Docker Model Runner:
docker model run hf.co/danangwijaya/IndoRetNet-Liputan6
End of training
Browse files- README.md +77 -0
- config.json +37 -0
- generation_config.json +6 -0
- model.safetensors +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- liputan6
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model-index:
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- name: IndoRetNet-Liputan6
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# IndoRetNet-Liputan6
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This model is a fine-tuned version of [](https://huggingface.co/) on the liputan6 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.4936
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0006
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 3.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:-----:|:---------------:|
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| 4.5053 | 0.17 | 1000 | 4.5145 |
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| 4.1281 | 0.34 | 2000 | 4.1702 |
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| 3.9452 | 0.52 | 3000 | 4.0094 |
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| 3.8302 | 0.69 | 4000 | 3.8972 |
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| 3.6955 | 0.86 | 5000 | 3.8144 |
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| 3.589 | 1.03 | 6000 | 3.7600 |
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| 3.5279 | 1.21 | 7000 | 3.7088 |
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| 3.4598 | 1.38 | 8000 | 3.6670 |
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| 3.4445 | 1.55 | 9000 | 3.6259 |
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| 3.4098 | 1.72 | 10000 | 3.5904 |
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| 3.3455 | 1.9 | 11000 | 3.5610 |
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| 3.2306 | 2.07 | 12000 | 3.5406 |
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| 3.261 | 2.24 | 13000 | 3.5216 |
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| 3.2204 | 2.41 | 14000 | 3.5111 |
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| 3.2321 | 2.59 | 15000 | 3.5001 |
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| 3.2514 | 2.76 | 16000 | 3.4941 |
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| 3.233 | 2.93 | 17000 | 3.4936 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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config.json
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{
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"activation_dropout": 0.1,
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"activation_fn": "swish",
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"architectures": [
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"RetNetForCausalLM"
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],
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"decoder_embed_dim": 512,
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"decoder_ffn_embed_dim": 864,
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"decoder_layers": 6,
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"decoder_normalize_before": true,
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"decoder_retention_heads": 8,
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"decoder_value_embed_dim": 864,
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"deepnorm": false,
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"drop_path_rate": 0.0,
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"dropout": 0.1,
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"eos_token_id": 0,
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"forward_impl": "parallel",
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"initializer_range": 0.02,
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"is_decoder": true,
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"layernorm_embedding": true,
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"layernorm_eps": 1e-06,
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"model_type": "retnet",
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"no_scale_embedding": false,
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"output_retentions": false,
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"pad_token_id": 0,
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"recurrent_chunk_size": 512,
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"subln": true,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.36.2",
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"use_cache": true,
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"use_ffn_rms_norm": false,
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"use_glu": true,
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"use_lm_decay": false,
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"vocab_size": 50257,
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"z_loss_coeff": 0.0
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}
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generation_config.json
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{
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"_from_model_config": true,
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"eos_token_id": 0,
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"pad_token_id": 0,
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"transformers_version": "4.36.2"
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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 sha256:4dfd1d4402f906514c3e0191309f57e2277578019f46fa4194fbe28605467d59
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size 282172696
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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| 5 |
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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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": "[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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"1": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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| 15 |
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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": "[MASK]",
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| 21 |
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"lstrip": false,
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| 22 |
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"normalized": false,
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| 23 |
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"rstrip": false,
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| 24 |
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"single_word": false,
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| 25 |
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"special": true
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| 26 |
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},
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"3": {
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| 28 |
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"content": "[CLS]",
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| 29 |
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"lstrip": false,
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| 30 |
+
"normalized": false,
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| 31 |
+
"rstrip": false,
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"single_word": false,
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| 33 |
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"special": true
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| 34 |
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},
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| 35 |
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"4": {
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| 36 |
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"content": "[SEP]",
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| 37 |
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"lstrip": false,
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| 38 |
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"normalized": false,
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| 39 |
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"rstrip": false,
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| 40 |
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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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| 44 |
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"clean_up_tokenization_spaces": true,
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| 45 |
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"cls_token": "[CLS]",
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| 46 |
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"do_basic_tokenize": true,
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| 47 |
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"do_lower_case": true,
|
| 48 |
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"mask_token": "[MASK]",
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| 49 |
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"model_max_length": 1000000000000000019884624838656,
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| 50 |
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"never_split": null,
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| 51 |
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"pad_token": "[PAD]",
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| 52 |
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"sep_token": "[SEP]",
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| 53 |
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"strip_accents": null,
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| 54 |
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"tokenize_chinese_chars": true,
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| 55 |
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:2231034ab91c23466ddeabc12138856f6ac0e8d9878d66c1aeecc179e49811e6
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size 4664
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vocab.txt
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