Feature Extraction
Transformers
Safetensors
code
ntv3_annotate
genomics
dna
gene-annotation
ntv3
long-range
custom_code
Instructions to use InstaDeepAI/NTv3_annot_insects with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/NTv3_annot_insects with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="InstaDeepAI/NTv3_annot_insects", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InstaDeepAI/NTv3_annot_insects", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from InstaDeepAI/NTv3_annot_insects: direct link, hf CLI and curl.
- Browser
- Download file 6.72 kB
-
https://huggingface.co/InstaDeepAI/NTv3_annot_insects/resolve/main/README.md
- Command line
-
hf download hf://InstaDeepAI/NTv3_annot_insects/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/InstaDeepAI/NTv3_annot_insects/resolve/main/README.md
6.72 kB
| library_name: transformers | |
| tags: | |
| - genomics | |
| - dna | |
| - gene-annotation | |
| - ntv3 | |
| - long-range | |
| license: other | |
| license_name: instadeep-open-model-licence | |
| license_link: https://huggingface.co/InstaDeepAI/NTv3_annot_insects/blob/main/MODEL-LICENSE.md | |
| language: | |
| - code | |
| extra_gated_heading: Request access to the NTv3 models | |
| extra_gated_description: >- | |
| One approval grants access to every gated NTv3 model. Requests are reviewed | |
| manually. | |
| extra_gated_prompt: >- | |
| The NTv3 weights are released under the | |
| [InstaDeep Open Model Licence](https://huggingface.co/InstaDeepAI/NTv3_annot_insects/blob/main/MODEL-LICENSE.md) for non-commercial use only, and its terms | |
| also cover the models' outputs. For commercial use, contact legal@instadeep.com. | |
| extra_gated_fields: | |
| Full name: text | |
| Affiliation: text | |
| Institutional email: text | |
| Organisation type: | |
| type: select | |
| options: | |
| - University | |
| - Public research institute | |
| - Non-profit | |
| - Hospital | |
| - Company | |
| - Other | |
| Country: country | |
| Intended use: text | |
| I will use NTv3 and its outputs for non-commercial purposes only, under the InstaDeep Open Model Licence: checkbox | |
| extra_gated_button_content: Request access | |
| # NTv3 Annotate — insects | |
| NTv3 gene-annotation model for insect genomes: 703M parameters, predicting one of 15 | |
| gene-structure classes at every base of a 1 Mb window. Modelling code is not in this | |
| repository: `config.json` resolves it from | |
| [`InstaDeepAI/NTv3_pretrained`](https://huggingface.co/InstaDeepAI/NTv3_pretrained) | |
| through `auto_map`, so loading requires `trust_remote_code=True`. | |
| > [!WARNING] | |
| > **Use 1,048,576 bp windows only.** This model was trained only on 1 Mb windows. | |
| > Shorter windows run without error, but annotation quality drops as the window gets | |
| > shorter. Tile long sequences with 1 Mb windows, keeping the central 75% of each, and | |
| > pad sequences shorter than 1 Mb with `N` up to 1,048,576 bp. The scores below were | |
| > measured this way. | |
| ## Test-species annotations | |
| Whole-genome predictions on the 10 held-out test species are in | |
| [`annot_results/`](annot_results/); its README gives provenance and scoring. | |
| | species | CDS F1 | gene F1 | GFF3 | | |
| |---|---:|---:|---| | |
| | *Bombyx mori* | 85.2 | 56.5 | [`annot_results/ntv3_bombyx_mori_GCF_030269925.1.gff3.gz`](annot_results/ntv3_bombyx_mori_GCF_030269925.1.gff3.gz) | | |
| | *Cataglyphis hispanica* | 87.8 | 63.9 | [`annot_results/ntv3_cataglyphis_hispanica_GCF_021464435.1.gff3.gz`](annot_results/ntv3_cataglyphis_hispanica_GCF_021464435.1.gff3.gz) | | |
| | *Colias croceus* | 88.1 | 62.6 | [`annot_results/ntv3_colias_croceus_GCF_905220415.1.gff3.gz`](annot_results/ntv3_colias_croceus_GCF_905220415.1.gff3.gz) | | |
| | *Danaus plexippus* | 89.4 | 65.7 | [`annot_results/ntv3_danaus_plexippus_GCF_018135715.1.gff3.gz`](annot_results/ntv3_danaus_plexippus_GCF_018135715.1.gff3.gz) | | |
| | *Leptidea sinapis* | 82.0 | 55.0 | [`annot_results/ntv3_leptidea_sinapis_GCF_905404315.1.gff3.gz`](annot_results/ntv3_leptidea_sinapis_GCF_905404315.1.gff3.gz) | | |
| | *Nymphalis io* (syn. *Aglais io*) | 89.2 | 66.0 | [`annot_results/ntv3_nymphalis_io_GCF_905147045.1.gff3.gz`](annot_results/ntv3_nymphalis_io_GCF_905147045.1.gff3.gz) | | |
| | *Osmia bicornis* | 86.3 | 58.7 | [`annot_results/ntv3_osmia_bicornis_GCF_907164935.1.gff3.gz`](annot_results/ntv3_osmia_bicornis_GCF_907164935.1.gff3.gz) | | |
| | *Tribolium castaneum* | 80.6 | 53.1 | [`annot_results/ntv3_tribolium_castaneum_GCF_031307605.1.gff3.gz`](annot_results/ntv3_tribolium_castaneum_GCF_031307605.1.gff3.gz) | | |
| | *Vanessa cardui* | 88.8 | 64.4 | [`annot_results/ntv3_vanessa_cardui_GCF_905220365.1.gff3.gz`](annot_results/ntv3_vanessa_cardui_GCF_905220365.1.gff3.gz) | | |
| | *Zerene cesonia* | 85.1 | 61.3 | [`annot_results/ntv3_zerene_cesonia_GCF_012273895.1.gff3.gz`](annot_results/ntv3_zerene_cesonia_GCF_012273895.1.gff3.gz) | | |
| ## Data and notebooks | |
| - **Benchmark data.** The test species' soft-masked genomes and reference | |
| annotations are in | |
| [`InstaDeepAI/NTv3_annot_dataset`](https://huggingface.co/datasets/InstaDeepAI/NTv3_annot_dataset) under | |
| `insects/test_species/`: the longest-isoform reference the scores above use | |
| (`reference_longest_isoform.gff3.gz`) and every annotated isoform | |
| (`reference_all_isoforms.gff3.gz`). | |
| - **Notebooks** in the [`ntv3`](https://huggingface.co/spaces/InstaDeepAI/ntv3) Space: | |
| [03a](https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_tutorials/03a_genome_annotation_training.ipynb) trains a plant annotation | |
| model from NTv3-tracks, | |
| [03b](https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_tutorials/03b_genome_annotation_decoding.ipynb) decodes per-base predictions | |
| into gene models, and | |
| [03c](https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_tutorials/03c_genome_annotation_evaluation.ipynb) scores a GFF3 against a | |
| reference the way the benchmark does. | |
| ## Model information | |
| - **Soft-masked input.** The tokenizer has 16 tokens: `A C G T N`, their lowercase | |
| (repeat-masked) forms, and special tokens. Lowercase bases carry their own token ids, | |
| so feed a soft-masked assembly (e.g. NCBI `_genomic.fna`, Ensembl `dna_sm`) for best | |
| results. An uppercase-only sequence runs without error but loses the repeat signal. | |
| `AutoTokenizer.from_pretrained(..., trust_remote_code=True)` loads it. | |
| - **Window.** 1,048,576 bp windows, keeping the central 0.75 of each | |
| (`ntv3.keep_target_center_fraction`, 0.75 by default in `config.json`). Keep both | |
| values: other settings give a plausible but wrong annotation, not an error. | |
| Pad sequences shorter than 1 Mb with `N`. | |
| - **No species conditioning.** Pass an empty list: `conditions_tokens=[]`. | |
| - **Precision.** Weights are fp32. For bf16, use `torch.autocast(device_type="cuda", | |
| dtype=torch.bfloat16)`; casting the model itself (`torch_dtype="bfloat16"`, | |
| `model.to(torch.bfloat16)`) fails. | |
| - **Decoding.** The model outputs per-base class logits, not genes. `config.json` | |
| holds the decoding HMM under `annotation` (states, transitions, splice-motif rules). | |
| Notebook [03b](https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_tutorials/03b_genome_annotation_decoding.ipynb) decodes the logits into | |
| gene models (GFF3) with it, and the NTv3-annotate pipeline (Home tab of the | |
| [`ntv3`](https://huggingface.co/spaces/InstaDeepAI/ntv3) Space) annotates a genome FASTA end to end. | |
| ## Licence | |
| Released under the [InstaDeep Open Model Licence](https://huggingface.co/InstaDeepAI/NTv3_annot_insects/blob/main/MODEL-LICENSE.md). The licence restricts **output** as | |
| well as weights, so predicted annotations carry the same terms. Read it before building | |
| on the model commercially. | |
| ## Citation | |
| Citation to be added on publication. | |