Instructions to use tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC_one_shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC_one_shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC_one_shot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC_one_shot") model = AutoModelForSequenceClassification.from_pretrained("tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC_one_shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "InstaDeepAI/nucleotide-transformer-500m-human-ref", | |
| "architectures": [ | |
| "EsmForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "emb_layer_norm_before": false, | |
| "esmfold_config": null, | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1280, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5120, | |
| "is_folding_model": false, | |
| "layer_norm_eps": 1e-12, | |
| "mask_token_id": 2, | |
| "max_position_embeddings": 1002, | |
| "model_type": "esm", | |
| "num_attention_heads": 20, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "tie_word_embeddings": false, | |
| "token_dropout": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.0.dev0", | |
| "use_cache": false, | |
| "use_flash_attention": false, | |
| "vocab_list": null, | |
| "vocab_size": 4105 | |
| } | |