reupload

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+model/stage1_model.bin filter=lfs diff=lfs merge=lfs -text
+pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

README.md

@@ -10,6 +10,37 @@ The bioremediation of environments contaminated with heavy metals is an importan
 
 Inference instruction will be released soon.
 
+## Interactive Demo
+
+You can try out the MetaLATTE model directly in your browser:
+
+<iframe
+	src="https://huggingface.co/spaces/ChatterjeeLab/MetaLATTE-demo"
+	frameborder="0"
+	width="850"
+	height="450"
+></iframe>
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModel, AutoConfig
+
+tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
+config = AutoConfig.from_pretrained("ChatterjeeLab/MetaLATTE")
+model = AutoModel.from_pretrained("ChatterjeeLab/MetaLATTE", config=config)
+
+# Use the model
+sequence = "AVYNIGWSFNVNGARGKSFRAGDVLVFKYIKGQHNVVAVNGRGYASCSAPRGARTYSSGQDRIKLTRGQNYFICSFPGHCGGGMKIAINAK"
+inputs = tokenizer(sequence, return_tensors="pt")
+raw_probs, predictions = model(**inputs)
+
+id2label = config.id2label
+predicted_labels = [id2label[i] for i, pred in enumerate(predictions[0]) if pred == 1]
+print(predicted_labels)
+['Cu']
+
+```
 
 # Repo Author
 - Yinuo Zhang ([email protected])

model/pytorch_model.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4a4b1eda0e66924a059f0bd2b6210142d24d37049bef6908ada05fb9195a83da
-size 5676931901

setup.py

@@ -11,7 +11,7 @@ setup(
         "torch==2.0.1+cu117",
         "numpy==1.22.4",
         "lightning==2.1.2",
-        "transformers==4.38.2",
+        "transformers>=4.38.2",
     ],
     python_requires='>=3.8',
 )