Create README.md

README.md

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+# ChemBERTa IUPAC Classifier
+
+This model is a fine-tuned version of [seyonec/ChemBERTa-zinc-base-v1](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1) for binary classification of chemical compounds based on their IUPAC names.
+
+## Model description
+
+This model uses ChemBERTa, a BERT-like model pre-trained on chemical structures, to classify molecules based on their IUPAC names. The model was fine-tuned on a custom dataset containing IUPAC names of molecules with binary labels.
+
+**Developed by:** xluobd
+
+**Model type:** RobertaForSequenceClassification
+
+**Language:** Chemical IUPAC nomenclature
+
+### How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("xluobd/chemberta-iupac-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("xluobd/chemberta-iupac-classifier")
+
+# Example IUPAC name
+iupac_name = "2-hydroxy-N,N,N-trimethylethan-1-aminium"
+
+# Tokenize and predict
+inputs = tokenizer(iupac_name, return_tensors="pt", padding=True, truncation=True, max_length=256)
+outputs = model(**inputs)
+probabilities = outputs.logits.softmax(dim=-1)
+prediction = probabilities.argmax().item()
+
+print(f"Prediction: {prediction}")
+print(f"Confidence: {probabilities[0][prediction].item():.4f}")