Instructions to use Taykhoom/structRFM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/structRFM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/structRFM", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/structRFM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 732 Bytes
933cd85 | 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 | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert_updated",
"auto_map": {
"AutoConfig": "configuration_bert_updated.BertUpdatedConfig",
"AutoModel": "modeling_bert.BertModel",
"AutoModelForMaskedLM": "modeling_bert.BertForMaskedLM"
},
"vocab_size": 10,
"hidden_size": 768,
"num_hidden_layers": 12,
"num_attention_heads": 12,
"intermediate_size": 3072,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 514,
"type_vocab_size": 2,
"initializer_range": 0.02,
"layer_norm_eps": 1e-12,
"pad_token_id": 0,
"model_max_length": 514,
"transformers_version": "4.57.6",
"_name_or_path": "Taykhoom/structRFM"
} |