Text Classification
Transformers
TensorBoard
Safetensors
English
bert
Generated from Trainer
custom_code
text-embeddings-inference
Instructions to use gyr66/relation_extraction_bert_base_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gyr66/relation_extraction_bert_base_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gyr66/relation_extraction_bert_base_uncased", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gyr66/relation_extraction_bert_base_uncased", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("gyr66/relation_extraction_bert_base_uncased", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +38 -38
config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"layer_norm_eps": 1e-12,
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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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"label2id": {
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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