Text Classification
Transformers
PyTorch
Safetensors
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use badalsahani/pdf-classification-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badalsahani/pdf-classification-multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="badalsahani/pdf-classification-multi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("badalsahani/pdf-classification-multi") model = AutoModelForSequenceClassification.from_pretrained("badalsahani/pdf-classification-multi") - Notebooks
- Google Colab
- Kaggle
File size: 1,592 Bytes
93daf48 | 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | {
"_name_or_path": "AutoTrain",
"_num_labels": 12,
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"directionality": "bidi",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "accountancy",
"1": "business-studies",
"2": "computer science",
"3": "economics",
"4": "english literature",
"5": "geography",
"6": "history",
"7": "mathematics",
"8": "political science",
"9": "psychology",
"10": "science",
"11": "sociology"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"accountancy": 0,
"business-studies": 1,
"computer science": 2,
"economics": 3,
"english literature": 4,
"geography": 5,
"history": 6,
"mathematics": 7,
"political science": 8,
"psychology": 9,
"science": 10,
"sociology": 11
},
"layer_norm_eps": 1e-12,
"max_length": 512,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"padding": "max_length",
"pooler_fc_size": 768,
"pooler_num_attention_heads": 12,
"pooler_num_fc_layers": 3,
"pooler_size_per_head": 128,
"pooler_type": "first_token_transform",
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.25.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 28996
}
|