Instructions to use Cippppy/mobilebert_run0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cippppy/mobilebert_run0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cippppy/mobilebert_run0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cippppy/mobilebert_run0") model = AutoModelForSequenceClassification.from_pretrained("Cippppy/mobilebert_run0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,309 Bytes
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"_name_or_path": "google/mobilebert-uncased",
"architectures": [
"MobileBertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_activation": false,
"classifier_dropout": null,
"embedding_size": 128,
"hidden_act": "relu",
"hidden_dropout_prob": 0.0,
"hidden_size": 512,
"id2label": {
"0": "AIRPLANE - Between Two Points",
"1": "DRONE - Follow Path",
"2": "HELICOPTER - Hover",
"3": "BIRDS - Circle",
"4": "DRONE - Travel"
},
"initializer_range": 0.02,
"intermediate_size": 512,
"intra_bottleneck_size": 128,
"key_query_shared_bottleneck": true,
"label2id": {
"AIRPLANE - Between Two Points": 0,
"BIRDS - Circle": 3,
"DRONE - Follow Path": 1,
"DRONE - Travel": 4,
"HELICOPTER - Hover": 2
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "mobilebert",
"normalization_type": "no_norm",
"num_attention_heads": 4,
"num_feedforward_networks": 4,
"num_hidden_layers": 24,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.37.2",
"trigram_input": true,
"true_hidden_size": 128,
"type_vocab_size": 2,
"use_bottleneck": true,
"use_bottleneck_attention": false,
"vocab_size": 30522
}
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