Instructions to use billfass/my_bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use billfass/my_bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="billfass/my_bert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("billfass/my_bert_model") model = AutoModelForMaskedLM.from_pretrained("billfass/my_bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
billfass commited on
Commit ·
e55d58e
1
Parent(s): 22bd428
Initial commit of BertModel and tokenizer
Browse files- config.json +10 -9
config.json
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{
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"architectures": [
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"BertForSequenceClassification"
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],
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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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"type_vocab_size": 2,
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"vocab_size": 119547,
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"model_name_or_path": "bert-base-multilingual-cased",
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"num_labels": 3,
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"finetuning_task": "text_classification",
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"id2label": {
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"0": "
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"1": "
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"2": "
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},
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"label2id": {
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}
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}
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{
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"_name_or_path": "bert-base-multilingual-cased",
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"architectures": [
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"BertForSequenceClassification"
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],
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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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"transformers_version": "4.x",
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"type_vocab_size": 2,
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"vocab_size": 119547,
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"num_labels": 3,
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"id2label": {
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"0": "class_0",
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"1": "class_1",
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"2": "class_2"
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},
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"label2id": {
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"class_0": 0,
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"class_1": 1,
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"class_2": 2
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}
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}
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