Instructions to use hfl/chinese-roberta-wwm-ext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/chinese-roberta-wwm-ext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/chinese-roberta-wwm-ext")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext") model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-roberta-wwm-ext") - Inference
- Notebooks
- Google Colab
- Kaggle
hfl-rc commited on
Commit ·
7e83adc
1
Parent(s): 3c83154
change pad_token_id from 1 to 0
Browse files- config.json +1 -1
config.json
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@@ -17,7 +17,7 @@
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id":
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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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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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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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