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
| { | |
| "_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 | |
| } | |