Text Classification
Transformers
Safetensors
GGUF
English
bert
emotion-classification
multilabel
goemotions
affective-computing
psychology
NLP
embeddings
symbolic-ai
poetic-ai
quantized
feature-extraction
Instructions to use willt-dc/Rosa-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use willt-dc/Rosa-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="willt-dc/Rosa-V1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("willt-dc/Rosa-V1") model = AutoModelForSequenceClassification.from_pretrained("willt-dc/Rosa-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use willt-dc/Rosa-V1 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf willt-dc/Rosa-V1:Q6_K # Run inference directly in the terminal: llama cli -hf willt-dc/Rosa-V1:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf willt-dc/Rosa-V1:Q6_K # Run inference directly in the terminal: llama cli -hf willt-dc/Rosa-V1:Q6_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf willt-dc/Rosa-V1:Q6_K # Run inference directly in the terminal: ./llama-cli -hf willt-dc/Rosa-V1:Q6_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf willt-dc/Rosa-V1:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf willt-dc/Rosa-V1:Q6_K
Use Docker
docker model run hf.co/willt-dc/Rosa-V1:Q6_K
- LM Studio
- Jan
- Ollama
How to use willt-dc/Rosa-V1 with Ollama:
ollama run hf.co/willt-dc/Rosa-V1:Q6_K
- Unsloth Studio
How to use willt-dc/Rosa-V1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for willt-dc/Rosa-V1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for willt-dc/Rosa-V1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for willt-dc/Rosa-V1 to start chatting
- Docker Model Runner
How to use willt-dc/Rosa-V1 with Docker Model Runner:
docker model run hf.co/willt-dc/Rosa-V1:Q6_K
- Lemonade
How to use willt-dc/Rosa-V1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull willt-dc/Rosa-V1:Q6_K
Run and chat with the model
lemonade run user.Rosa-V1-Q6_K
List all available models
lemonade list
- Atomic Chat
Upload folder using huggingface_hub
Browse files- config.json +87 -0
- model.safetensors +3 -0
- special_tokens_map.json +7 -0
- tokenizer_config.json +57 -0
- vocab.txt +0 -0
config.json
ADDED
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{
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"_name_or_path": "bert-base-uncased",
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"architectures": [
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"BertForSequenceClassification"
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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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"id2label": {
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"0": "admiration",
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"1": "amusement",
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"2": "anger",
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"3": "annoyance",
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"4": "approval",
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"5": "caring",
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"6": "confusion",
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"7": "curiosity",
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"8": "desire",
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"9": "disappointment",
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"10": "disapproval",
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"11": "disgust",
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"12": "embarrassment",
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"13": "excitement",
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"14": "fear",
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"15": "gratitude",
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"16": "grief",
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"17": "joy",
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"18": "love",
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"19": "nervousness",
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"20": "optimism",
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"21": "pride",
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"22": "realization",
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"23": "relief",
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"24": "remorse",
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"25": "sadness",
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"26": "surprise",
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"27": "neutral"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"admiration": 0,
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"amusement": 1,
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"anger": 2,
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"annoyance": 3,
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"approval": 4,
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"caring": 5,
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"confusion": 6,
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"curiosity": 7,
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"desire": 8,
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"disappointment": 9,
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"disapproval": 10,
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"disgust": 11,
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"embarrassment": 12,
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"excitement": 13,
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"fear": 14,
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"gratitude": 15,
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"grief": 16,
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"joy": 17,
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"love": 18,
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"nervousness": 19,
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"optimism": 20,
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"pride": 21,
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"realization": 22,
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"relief": 23,
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"remorse": 24,
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"sadness": 25,
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"surprise": 26,
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"neutral": 27
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},
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"layer_norm_eps": 1e-12,
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| 75 |
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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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| 78 |
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"num_hidden_layers": 12,
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| 79 |
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"pad_token_id": 0,
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| 80 |
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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| 83 |
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522,
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"num_labels": 28
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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:f8a85bb2dced8e1d4af34ce4ee98e35940503ea176c43efffdcdc2d68fd78f20
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size 438038624
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special_tokens_map.json
ADDED
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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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"sep_token": "[SEP]",
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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": "[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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"100": {
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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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"101": {
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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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"102": {
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"content": "[SEP]",
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"lstrip": false,
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| 30 |
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"normalized": false,
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| 31 |
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"rstrip": false,
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| 32 |
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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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"103": {
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"content": "[MASK]",
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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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| 41 |
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"special": true
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| 42 |
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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,
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| 48 |
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"mask_token": "[MASK]",
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| 49 |
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"model_max_length": 512,
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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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| 56 |
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"unk_token": "[UNK]"
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}
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vocab.txt
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