Instructions to use lukecarlate/SecBERT_CM_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/SecBERT_CM_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/SecBERT_CM_Num")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/SecBERT_CM_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/SecBERT_CM_Num") - Notebooks
- Google Colab
- Kaggle
lukecarlate commited on
Commit ·
9549ea5
1
Parent(s): 8a5fae3
Add more files
Browse files- config.json +26 -0
- elapsed-time.log +1 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
config.json
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{
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"_name_or_path": "C:/Users/user/Incorporation_of_Company-Related_Factual_Knowledge_into_Pre-trained_Language_Models/postTrained_SecBERT_CompanyNameMasking",
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"architectures": [
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"BertForMaskedLM"
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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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"initializer_range": 0.02,
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"intermediate_size": 3072,
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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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"torch_dtype": "float32",
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"transformers_version": "4.14.1",
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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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elapsed-time.log
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Time elapsed: 4:25:37.642047
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:4d482906f25d003539744809a63d9d028149680c7fe7c53baee4da4fdf5faa62
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size 436538169
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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, "do_basic_tokenize": true, "never_split": null, "special_tokens_map_file": "/home/jihye/.cache/huggingface/transformers/2d5de25509a0e8b833df58416f069106d460560c73df71f1ab0eab96530549fd.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "C:/Users/user/Incorporation_of_Company-Related_Factual_Knowledge_into_Pre-trained_Language_Models/postTrained_SecBERT_CompanyNameMasking", "tokenizer_class": "BertTokenizer"}
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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:ac7cca96d426c9d9c989671c009b74ad79b518d5ca2f9a2ffdcf6762b7d32e5f
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size 3003
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vocab.txt
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