Instructions to use naveenVakada/tiny_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naveenVakada/tiny_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="naveenVakada/tiny_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("naveenVakada/tiny_model") model = AutoModelForSequenceClassification.from_pretrained("naveenVakada/tiny_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,177 Bytes
30aed86 | 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 | {
"_name_or_path": "prajjwal1/bert-tiny",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 128,
"id2label": {
"0": "Music recommendation",
"1": "Music identification",
"2": "ASR whisper",
"3": "Speaker ID, Diarization, counting",
"4": "Audio/Text To Audio",
"5": "Source separation/removal",
"6": "Unsupported"
},
"initializer_range": 0.02,
"intermediate_size": 512,
"label2id": {
"ASR whisper": 2,
"Audio/Text To Audio": 4,
"Music identification": 1,
"Music recommendation": 0,
"Source separation/removal": 5,
"Speaker ID, Diarization, counting": 3,
"Unsupported": 6
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 2,
"num_hidden_layers": 2,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.41.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
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