Text Classification
Transformers
PyTorch
megatron-bert
prokbert
bioinformatics
genomics
sequence embedding
genomic language models
nucleotide
dna-sequence
promoter-prediction
phage
Instructions to use neuralbioinfo/prokbert-mini-c-phage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralbioinfo/prokbert-mini-c-phage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralbioinfo/prokbert-mini-c-phage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralbioinfo/prokbert-mini-c-phage") model = AutoModelForSequenceClassification.from_pretrained("neuralbioinfo/prokbert-mini-c-phage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ligeti Balázs commited on
Commit ·
6db16f2
1
Parent(s): aac71c7
Finetuned prokbert-c model for phage identification
Browse files- config.json +25 -0
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "/home/c_evolb/c_evolm_scratch/finetuned_models/prokbert-mini-L512B-k1s1/checkpoint-1",
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"architectures": [
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"MegatronBertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 2048,
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"model_type": "megatron-bert",
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"num_attention_heads": 6,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "relative_key_query",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.33.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 20
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:bcc5f4955fe97e959c0acb318b7b4d03562f894ef4e29e4445f77c5eb2c2a372
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size 99939078
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