Paper: Machine Learning Dataset and Benchmark for Accurate T Cell Receptor–pHLA Binding Prediction
Github repo: https://github.com/Jiadong001/Hi-TpH
| Level | Directory | Input components |
|---|---|---|
| I | level1 | Peptide + TCR CDR3β |
| II | level2 | Peptide + HLA + TCR CDR3β |
| III | level3 | Peptide + HLA + TCR CDR3α + TCR CDR3β |
| IV | level4 | Peptide + HLA + full TCR α and β variable-region sequences |
Each level contains the same nine model subdirectories. Each model subdirectory contains a checkpoint (ckpt.pkl) and its configuration (config.json). The root-level config.json describes the input components for the four levels.
Hi-TPH-PLMs/
├── README.md
├── config.json
├── level1/
│ ├── esm2-8M/
│ │ ├── ckpt.pkl
│ │ └── config.json
│ ├── esm2-35M/
│ ├── esm2-150M/
│ ├── esm2-35M/
│ ├── AMPLIFY-120M-base/
│ ├── AMPLIFY-120M/
│ ├── AMPLIFY-350M/
│ ├── protbert/
│ └── tape/
├── level2/ # Same model subdirectories as level1
├── level3/ # Same model subdirectories as level1
└── level4/ # Same model subdirectories as level1
Install the download library in your Python environment:
python -m pip install --upgrade huggingface_hub
Choose one of the following examples according to the checkpoints you need. Files are saved under ./Hi-TPH-PLMs with the repository's directory structure preserved.
For example, download the Level IV ESM2-35M checkpoint and its configuration:
from pathlib import Path
from huggingface_hub import snapshot_download
download_dir = snapshot_download(
repo_id="Jiadong001/Hi-TPH-PLMs",
allow_patterns=["level4/esm2-35M/*"],
local_dir="./Hi-TPH-PLMs",
)
model_dir = Path(download_dir) / "level4" / "esm2-35M"
print("Checkpoint:", model_dir / "ckpt.pkl")
print("Configuration:", model_dir / "config.json")
Replace level4/esm2-35M with any level and model directory listed above, such as level1/esm2-8M or level3/AMPLIFY-120M.
For example, download all nine models for Level IV:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Jiadong001/Hi-TPH-PLMs",
allow_patterns=["level4/*"],
local_dir="./Hi-TPH-PLMs",
)
The complete collection contains 36 checkpoints across four levels (approximately 29.02 GiB of weights).
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Jiadong001/Hi-TPH-PLMs",
local_dir="./Hi-TPH-PLMs",
)
These examples only download the checkpoint files. For model construction, input preprocessing, and checkpoint loading, refer to the Hi-TPH code repository.
For additional download options, see the Hugging Face download guide.
5 commits
Paper: Machine Learning Dataset and Benchmark for Accurate T Cell Receptor–pHLA Binding Prediction
Github repo: https://github.com/Jiadong001/Hi-TpH
| Level | Directory | Input components |
|---|---|---|
| I | level1 | Peptide + TCR CDR3β |
| II | level2 | Peptide + HLA + TCR CDR3β |
| III | level3 | Peptide + HLA + TCR CDR3α + TCR CDR3β |
| IV | level4 | Peptide + HLA + full TCR α and β variable-region sequences |
Each level contains the same nine model subdirectories. Each model subdirectory contains a checkpoint (ckpt.pkl) and its configuration (config.json). The root-level config.json describes the input components for the four levels.
Hi-TPH-PLMs/
├── README.md
├── config.json
├── level1/
│ ├── esm2-8M/
│ │ ├── ckpt.pkl
│ │ └── config.json
│ ├── esm2-35M/
│ ├── esm2-150M/
│ ├── esm2-35M/
│ ├── AMPLIFY-120M-base/
│ ├── AMPLIFY-120M/
│ ├── AMPLIFY-350M/
│ ├── protbert/
│ └── tape/
├── level2/ # Same model subdirectories as level1
├── level3/ # Same model subdirectories as level1
└── level4/ # Same model subdirectories as level1
Install the download library in your Python environment:
python -m pip install --upgrade huggingface_hub
Choose one of the following examples according to the checkpoints you need. Files are saved under ./Hi-TPH-PLMs with the repository's directory structure preserved.
For example, download the Level IV ESM2-35M checkpoint and its configuration:
from pathlib import Path
from huggingface_hub import snapshot_download
download_dir = snapshot_download(
repo_id="Jiadong001/Hi-TPH-PLMs",
allow_patterns=["level4/esm2-35M/*"],
local_dir="./Hi-TPH-PLMs",
)
model_dir = Path(download_dir) / "level4" / "esm2-35M"
print("Checkpoint:", model_dir / "ckpt.pkl")
print("Configuration:", model_dir / "config.json")
Replace level4/esm2-35M with any level and model directory listed above, such as level1/esm2-8M or level3/AMPLIFY-120M.
For example, download all nine models for Level IV:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Jiadong001/Hi-TPH-PLMs",
allow_patterns=["level4/*"],
local_dir="./Hi-TPH-PLMs",
)
The complete collection contains 36 checkpoints across four levels (approximately 29.02 GiB of weights).
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Jiadong001/Hi-TPH-PLMs",
local_dir="./Hi-TPH-PLMs",
)
These examples only download the checkpoint files. For model construction, input preprocessing, and checkpoint loading, refer to the Hi-TPH code repository.
For additional download options, see the Hugging Face download guide.
5 commits