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Browse files- README.md +45 -0
- config.json +40 -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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license: apache-2.0
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tags:
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- bert
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- kcbert
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- kor-hate-sentence
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- sentimental-analysis
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---
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# SJ-Donald/kor-hate-sentence
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SJ-Donald/kor-hate-sentence is pretrained model using follow:
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## Models
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* [beomi/kcbert-large](https://huggingface.co/beomi/kcbert-large)
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## Datasets
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* [SJ-Donald/kor-hate-sentence](https://huggingface.co/datasets/SJ-Donald/kor-hate-sentence)
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## How to use
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```Python
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from transformers import TextClassificationPipeline, BertForSequenceClassification, AutoTokenizer+
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model_name = 'SJ-Donald/kor-hate-sentence-large'
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model = BertForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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pipe = TextClassificationPipeline(
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model = model,
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tokenizer = tokenizer,
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device = 0, # cpu: -1, gpu: gpu number
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return_all_scores = True,
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function_to_apply = 'sigmoid'
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)
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for result in pipe("이딴 게임할 거면 방송 그만해라 어휴")[0]:
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print(result)
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{'label': 'hate', 'score': 0.016597675159573555}
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{'label': 'clean', 'score': 0.9842987060546875}
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```
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config.json
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{
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"_name_or_path": "beomi/kcbert-large",
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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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"directionality": "bidi",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "hate",
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"1": "clean"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"clean": 1,
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"hate": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 300,
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"model_type": "bert",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"problem_type": "multi_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30000
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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 sha256:254c49e80b045593739e0a1bd38f051b62dae738ff73b79bbfd6bc3cd99d6ca2
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size 1337616296
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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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"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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"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": "[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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"3": {
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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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"4": {
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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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"model_max_length": 300,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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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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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:bf886eddc1d43f2f0efdee75b2595756368c96bc5f79257d8ba9c3bc152d5577
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size 4600
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vocab.txt
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