Fill-Mask
Transformers
Safetensors
English
roberta
genomics
population-genetics
axial-attention
self-supervised
natural-selection
haplotype
Instructions to use leonzong/popf-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leonzong/popf-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="leonzong/popf-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("leonzong/popf-small") model = AutoModelForMaskedLM.from_pretrained("leonzong/popf-small") - Notebooks
- Google Colab
- Kaggle
File size: 645 Bytes
af2afa2 | 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 | {
"architectures": [
"HapbertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"axial": true,
"bos_token_id": 2,
"classifier_dropout": null,
"eos_token_id": 3,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 512,
"initializer_range": 0.02,
"intermediate_size": 1024,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "roberta",
"num_attention_heads": 4,
"num_hidden_layers": 4,
"pad_token_id": 5,
"position_embedding_type": "haplo",
"torch_dtype": "float32",
"transformers_version": "4.55.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 6
} |