Text Classification
Transformers
PyTorch
Safetensors
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Showroom/gender_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Showroom/gender_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Showroom/gender_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Showroom/gender_classifier") model = AutoModelForSequenceClassification.from_pretrained("Showroom/gender_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,014 Bytes
d98d9ca | 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 41 42 43 | {
"_name_or_path": "AutoTrain",
"_num_labels": 4,
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "gender could not be deduced",
"1": "men's",
"2": "unisex",
"3": "women's"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"gender could not be deduced": 0,
"men's": 1,
"unisex": 2,
"women's": 3
},
"layer_norm_eps": 1e-12,
"max_length": 256,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"padding": "max_length",
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.29.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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