Instructions to use Mozart-coder/BERT_Dec-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_Dec-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_Dec-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_Dec-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_Dec-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "armheb/DNA_bert_6", | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_ids": 0, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "num_rnn_layer": 1, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "rnn": "lstm", | |
| "rnn_dropout": 0.0, | |
| "rnn_hidden": 768, | |
| "split": 10, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.23.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 4101 | |
| } | |