Text Classification
Transformers
TensorFlow
TensorBoard
Safetensors
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use PDAP/url-relevance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PDAP/url-relevance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PDAP/url-relevance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PDAP/url-relevance") model = AutoModelForSequenceClassification.from_pretrained("PDAP/url-relevance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 955 Bytes
178e145 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"_name_or_path": "./url_relevance/checkpoint-1000",
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 2048,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tabular_config": {
"cat_feat_dim": 0,
"categorical_bn": true,
"combine_feat_method": "text_only",
"gating_beta": 0.2,
"hidden_dropout_prob": 0.2,
"mlp_act": "relu",
"mlp_division": 4,
"mlp_dropout": 0.1,
"num_labels": 2,
"numerical_bn": true,
"numerical_feat_dim": 0,
"text_feat_dim": 768,
"use_simple_classifier": true
},
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.38.2",
"vocab_size": 30522
}
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