Instructions to use LequeuISIR/AP-NeoBERT-768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LequeuISIR/AP-NeoBERT-768 with Transformers:
# Load model directly from transformers import NeoBERT model = NeoBERT.from_pretrained("LequeuISIR/AP-NeoBERT-768", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "AP_embeddings": true, | |
| "architectures": [ | |
| "NeoBERT" | |
| ], | |
| "attention_activation": "softmax", | |
| "attention_ativation": "softmax", | |
| "attention_probs_dropout_prob": 0.1, | |
| "base_scale": 0.03227486121839514, | |
| "classifier_init_range": 0.02, | |
| "decoder_init_range": 0.02, | |
| "dim_head": 128, | |
| "dropout_prob": 0, | |
| "embedding_init_range": 0.02, | |
| "entropy_regularization_lambda": 0.01, | |
| "flash_attention": false, | |
| "hidden_act": "swiglu", | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "kwargs": { | |
| "attention_ativation": "softmax", | |
| "classifier_init_range": 0.02, | |
| "entropy_regularization_lambda": 0.01 | |
| }, | |
| "max_length": 512, | |
| "mix_attentions": "sum", | |
| "mixed_feed_forward": true, | |
| "model_type": "neobert", | |
| "ngpt": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 6, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 0, | |
| "pos_dropout_prob": 0.1, | |
| "pos_intermediate_size": 1536, | |
| "pos_size": 384, | |
| "positional_embed_init": "random", | |
| "posneobert": false, | |
| "random_offset": false, | |
| "relative_pos_bias": false, | |
| "rms_norm": true, | |
| "rope": false, | |
| "scale_QK_dim": true, | |
| "share_pos_embeds_in_heads": false, | |
| "shared_pos_keys": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.3", | |
| "untie_cls": false, | |
| "use_only_sem_for_decoding": false, | |
| "vocab_size": 30522 | |
| } | |