Push model using huggingface_hub.

README.md

@@ -1,91 +1,10 @@
 ---
-datasets:
-- andrewdalpino/AmiGO-Boost
-metrics:
-- precision
-- recall
-- f1
-base_model:
-- EvolutionaryScale/esmc-300m-2024-12
-pipeline_tag: text-classification
 tags:
-- gene-ontology
+- model_hub_mixin
+- pytorch_model_hub_mixin
 ---
 
-# ESMC Protein Function Predictor
-
-An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM Cambrian Transformer architecture, pre-trained on [UniRef](https://www.uniprot.org/help/uniref), [MGnify](https://www.ebi.ac.uk/metagenomics), and the Joint Genome Institute's database and fine-tuned on the [AmiGO Boost](https://huggingface.co/datasets/andrewdalpino/AmiGO-Boost) protein function dataset, this model predicts the GO subgraph for a particular protein sequence - giving you insight into the molecular function, biological process, and location of the activity inside the cell.
-
-## What are GO terms?
-
-> "The Gene Ontology (GO) is a concept hierarchy that describes the biological function of genes and gene products at different levels of abstraction (Ashburner et al., 2000). It is a good model to describe the multi-faceted nature of protein function."
-
-> "GO is a directed acyclic graph. The nodes in this graph are functional descriptors (terms or classes) connected by relational ties between them (is_a, part_of, etc.). For example, terms 'protein binding activity' and 'binding activity' are related by an is_a relationship; however, the edge in the graph is often reversed to point from binding towards protein binding. This graph contains three subgraphs (subontologies): Molecular Function (MF), Biological Process (BP), and Cellular Component (CC), defined by their root nodes. Biologically, each subgraph represent a different aspect of the protein's function: what it does on a molecular level (MF), which biological processes it participates in (BP) and where in the cell it is located (CC)."
-
-From [CAFA 5 Protein Function Prediction](https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/data)
-
-## Pretrained Models
-
-The following pretrained models are available on HuggingFace Hub.
-
-| Name | Embedding Dim. | Attn. Heads | Encoder Layers | Context Length | QAT | Total Parameters |
-|---|---|---|---|---|---|---|
-| [andrewdalpino/ESMC-300M-Protein-Function](https://huggingface.co/andrewdalpino/ESMC-300M-Protein-Function) | 960 | 15 | 30 | 2048 | None | 361M |
-| [andrewdalpino/ESMC-300M-QAT-Protein-Function](https://huggingface.co/andrewdalpino/ESMC-300M-QAT-Protein-Function) | 960 | 15 | 30 | 2048 | int8w | 361M |
-| [andrewdalpino/ESMC-600M-Protein-Function](https://huggingface.co/andrewdalpino/ESMC-600M-Protein-Function) | 1152 | 18 | 36 | 2048 | None  | 644M |
-| [andrewdalpino/ESMC-600M-QAT-Protein-Function](https://huggingface.co/andrewdalpino/ESMC-600M-QAT-Protein-Function) | 1152 | 18 | 36 | 2048 | int8w | 644M |
-
-## Basic Pretrained Example
-
-First, install the `esmc_function_classifier` package using [pip](https://pypi.org/project/pip/).
-
-```sh
-pip install esmc_function_classifier obonet
-```
-
-Then, we'll load the model weights from HuggingFace Hub and the GO graph using `obonet`, tokenize the amino acid sequence, and infer the GO subgraph.
-
-```python
-import torch
-
-import obonet
-
-from esm.tokenization import EsmSequenceTokenizer
-
-from esmc_function_classifier.model import EsmcGoTermClassifier
-
-
-model_name = "andrewdalpino/ESMC-300M-Protein-Function"
-
-# Visit https://geneontology.org/docs/download-ontology/ to download.
-go_db_path = "./dataset/go-basic.obo"
-
-sequence = "MPPKGHKKTADGDFRPVNSAGNTIQAKQKYSIDDLLYPKSTIKNLAKETLPDDAIISKDALTAIQRAATLFVSYMASHGNASAEAGGRKKIT"
-
-top_p = 0.5
-
-graph = obonet.read_obo(go_db_path)
-
-tokenizer = EsmSequenceTokenizer()
-
-model = EsmcGoTermClassifier.from_pretrained(model_name)
-
-model.load_gene_ontology(graph)
-
-out = tokenizer(sequence, max_length=2048, truncation=True)
-
-input_ids = torch.tensor(out["input_ids"], dtype=torch.int64)
-
-subgraph, go_term_probabilities = model.predict_subgraph(
-    input_ids, top_p=top_p
-)
-```
-
-## Code Repository
-
-The training code can be found at [https://github.com/andrewdalpino/ESMC-Function-Classifier](https://github.com/andrewdalpino/ESMC-Function-Classifier).
-
-## References:
-
->- T. Hayes, et al. Simulating 500 million years of evolution with a language model, 2024.
->- M. Ashburner, et al. Gene Ontology: tool for the unification of biology, 2000.
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: [More Information Needed]
+- Paper: [More Information Needed]
+- Docs: [More Information Needed]

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