Instructions to use gutierrez310/som_ml_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gutierrez310/som_ml_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gutierrez310/som_ml_model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gutierrez310/som_ml_model") model = AutoModel.from_pretrained("gutierrez310/som_ml_model") - Notebooks
- Google Colab
- Kaggle
File size: 694 Bytes
60b29c7 | 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 | {
"add_cross_attention": false,
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 128,
"initializer_range": 0.02,
"intermediate_size": 512,
"is_decoder": false,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 2,
"num_hidden_layers": 2,
"pad_token_id": 0,
"tie_word_embeddings": true,
"transformers_version": "5.5.4",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
|