Instructions to use tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS2_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_BioS2_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_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot") model = AutoModelForSequenceClassification.from_pretrained("tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 873 Bytes
001580a | 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 | {
"_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
}
|