Instructions to use StanfordSCALE/relationship_classifier_multi_retrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StanfordSCALE/relationship_classifier_multi_retrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="StanfordSCALE/relationship_classifier_multi_retrained")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("StanfordSCALE/relationship_classifier_multi_retrained") model = AutoModelForSequenceClassification.from_pretrained("StanfordSCALE/relationship_classifier_multi_retrained") - Notebooks
- Google Colab
- Kaggle
File size: 888 Bytes
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"architectures": [
"RobertaForSequenceClassification"
],
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"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
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},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.51.3",
"type_vocab_size": 1,
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}
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