| | --- |
| | library_name: transformers |
| | tags: [] |
| | --- |
| | |
| | ## THIS PROTEIN VEC ADAPTATION WAS MODIFIED FROM https://github.com/tymor22/protein-vec |
| |
|
| | All credit for the original work goes to Tymor Hamamsy and the following authors of this paper https://www.biorxiv.org/content/10.1101/2023.11.26.568742v1 |
| |
|
| | We have added a Huggingface compatible wrapper for the model in protvec.py |
| |
|
| | Please consider liking the model page and starring the github repo if you are going to use it :) |
| |
|
| | ``` |
| | https://huggingface.co/lhallee/ProteinVec |
| | https://github.com/lhallee/ProteinVecHuggingface |
| | ``` |
| |
|
| | Clone and install |
| | ``` |
| | git clone https://github.com/lhallee/ProteinVecHuggingface.git |
| | pip install torch pytorch_lightning transformers |
| | ``` |
| |
|
| | To use from hugggingface |
| | ``` |
| | from transformers import T5Tokenizer |
| | from protvec import ProteinVec, ProteinVecConfig |
| | |
| | tokenizer = T5Tokenizer.from_pretrained('lhallee/ProteinVec') |
| | model = ProteinVec.from_pretrained('lhallee/ProteinVec', config=ProteinVecConfig()) |
| | ``` |
| |
|
| | Embed a single sequence with ```embed``` |
| | ``` |
| | model.to_eval() |
| | model = model.cuda() # remove if cpu inference |
| | embedding = model.embed('SEQWENCE').detach().cpu() # torch.tensor(1, 512) |
| | ``` |
| |
|
| | Use a particular AspectVec by setting the ```inference_mask``` |
| | ``` |
| | model.aspect_to_keys_dict # dictionary showing the aspects |
| | ### The model is set to ALL by default to use full ProteinVec |
| | model.inference_mask = model.get_mask('EC') # for Enzyme Comission AspectVec |
| | embedding = model.embed(...) |
| | ``` |
| |
|
| | To half precision weights |
| | ``` |
| | model.to_half() |
| | ``` |
| |
|
| |
|
| | ## The license for the protein vec code |
| |
|
| | ### BSD 3-Clause License |
| |
|
| | Copyright (c) 2023, Tymor Hamamsy |
| |
|
| | Redistribution and use in source and binary forms, with or without |
| | modification, are permitted provided that the following conditions are met: |
| |
|
| | 1. Redistributions of source code must retain the above copyright notice, this |
| | list of conditions and the following disclaimer. |
| |
|
| | 2. Redistributions in binary form must reproduce the above copyright notice, |
| | this list of conditions and the following disclaimer in the documentation |
| | and/or other materials provided with the distribution. |
| |
|
| | 3. Neither the name of the copyright holder nor the names of its |
| | contributors may be used to endorse or promote products derived from |
| | this software without specific prior written permission. |
| |
|
| | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE |
| | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE |
| | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL |
| | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR |
| | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER |
| | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, |
| | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE |
| | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
