Instructions to use TCMVince/HOP4NLP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TCMVince/HOP4NLP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="TCMVince/HOP4NLP2", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("TCMVince/HOP4NLP2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 901 Bytes
587e3b7 | 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 | {
"_name_or_path": "FOR_JOSEPH/NRJ-BASE-125K",
"activation": "relu",
"alpha": 1.0,
"architectures": [
"BertEnergyModelForMaskedLM"
],
"attention_probs_dropout_prob": 0.0,
"auto_map": {
"AutoConfig": "hf_configuration.BertEnergyConfig",
"AutoModelForMaskedLM": "mlm.BertEnergyModelForMaskedLM"
},
"beta": null,
"bias": true,
"compile": true,
"embedding_dim": 768,
"hidden_dropout_prob": 0.1,
"hidden_size": 3072,
"initializer_hopfield_range": 0.002,
"initializer_range": 0.02,
"intermediate_size": 12288,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert_energy",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 3,
"path": null,
"positional": true,
"share_layers": true,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.49.0",
"vocab_size": 30000
}
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