init
Browse files- README.md +2 -0
- config.json +43 -0
- example.py +10 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
README.md
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license: cc-by-nc-sa-4.0
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license: cc-by-nc-sa-4.0
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The current version of code is based on https://huggingface.co/nateraw/bert-base-uncased-emotion and will be improved gradually.
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config.json
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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": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2",
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"3": "LABEL_3",
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"4": "LABEL_4",
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"5": "LABEL_5"
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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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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_2": 2,
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"LABEL_3": 3,
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"LABEL_4": 4,
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"LABEL_5": 5
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},
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"layer_norm_eps": 1e-12,
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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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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.19.2",
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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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}
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example.py
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from transformers import AutoModel
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if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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model = AutoModel.from_pretrained("sabersol/bert-base-uncased-emotion")
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pred = transformers.pipeline("text-classification", model=model, tokenizer=tokenizer, device=0, return_all_scores=True)
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example = "The music I am listening to is so energetic and makes me motivated."
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results = pred(example)
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print(results)
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:892b89d11f17b4c9ca7b429a72a1edab084f06a546e337090461616262c70935
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size 438018413
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
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tokenizer_config.json
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{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-uncased", "tokenizer_class": "BertTokenizer"}
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vocab.txt
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