Text Classification
Transformers
Safetensors
English
bert
multi-label
theme_detection
mentorship
entrepreneurship
startup success
json automation
text-embeddings-inference
Instructions to use 4nkh/theme_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 4nkh/theme_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="4nkh/theme_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("4nkh/theme_model") model = AutoModelForSequenceClassification.from_pretrained("4nkh/theme_model") - Notebooks
- Google Colab
- Kaggle
File size: 874 Bytes
6789740 | 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 | {
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"dtype": "float32",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "mentorship",
"1": "entrepreneurship",
"2": "startup success"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"entrepreneurship": 1,
"mentorship": 0,
"startup success": 2
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "multi_label_classification",
"transformers_version": "4.57.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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