Push model using huggingface_hub.

README.md

@@ -7,42 +7,7 @@ tags:
 - pytorch_model_hub_mixin
 ---
 
-# GSFM
-
-Trained on millions of gene sets automatically extracted from literature and raw RNA-seq data, GSFM learns to recover held-out genes from gene sets. The resulting model exhibits state of the art performance on gene function prediction.
-
-## Website
-
-<https://gsfm.maayanlab.cloud/>
-
-## Usage
-
-```bash
-# install gsfm python library from its source on huggingface
-GIT_LFS_SKIP_SMUDGE=1 pip install git+https://huggingface.co/maayanlab/gsfm
-```
-
-```python
-import torch
-from gsfm import Vocab, GSFM
-
-# load gsfm vocabulary and model weights
-vocab = Vocab.from_pretrained('maayanlab/gsfm')
-gsfm = GSFM.from_pretrained('maayanlab/gsfm')
-
-# convert gene symbols into token ids
-token_ids = torch.tensor(vocab(['ACE1', 'ACE2']))[None, :]
-
-# use model to predict missing genes from the set
-logits = torch.squeeze(gsfm(token_ids))
-top_10 = sorted(zip(logits, vocab.vocab))[-10:]
-top_10
-
-# get gene embedding
-gene_embeddings = gsfm.embedding(token_ids)
-gene_embeddings
-
-# get model middle layer
-gene_set_encoding = gsfm.encode(token_ids)
-gene_set_encoding
-```
+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]

config.json

@@ -1,5 +1,8 @@
 {
-  "d_model": 1024,
+  "d_model": 256,
   "depth": 2,
-  "vocab_size": 58716
+  "dropout": 0.2,
+  "partition": 0,
+  "vocab_size": 19400,
+  "weighted_loss": "none"
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0dc90c1e71a3a89128ae808c836bc72ed048c0df3784363d7a48e698a600bea1
-size 725935944
+oid sha256:7c8a81e269a652481e0fc1846f43eb4ab28c62a0e97c0f48e929a2425ee98921
+size 60465656