Update README to include transformers version requirement

README.md

@@ -124,6 +124,12 @@ model-index:
 ModChemBERT is a ModernBERT-based chemical language model (CLM), trained on SMILES strings for masked language modeling (MLM) and downstream molecular property prediction (classification & regression).
 
 ## Usage
+Install the `transformers` library starting from v4.56.1:
+
+```bash
+pip install -U transformers>=4.56.1
+```
+
 ### Load Model
 ```python
 from transformers import AutoModelForMaskedLM, AutoTokenizer
@@ -146,19 +152,6 @@ fill = pipeline("fill-mask", model=model, tokenizer=tokenizer)
 print(fill("c1ccccc1[MASK]"))
 ```
 
-## Intended Use
-* Primary: Research and development for molecular property prediction, experimentation with pooling strategies, and as a foundational model for downstream applications.
-* Appropriate for: Binary / multi-class classification (e.g., toxicity, activity) and single-task or multi-task regression (e.g., solubility, clearance) after fine-tuning.
-* Not intended for generating novel molecules.
-
-## Limitations
-- Out-of-domain performance may degrade for: very long (>128 token) SMILES, inorganic / organometallic compounds, polymers, or charged / enumerated tautomers are not well represented in training.
-- No guarantee of synthesizability, safety, or biological efficacy.
-
-## Ethical Considerations & Responsible Use
-- Potential biases arise from training corpora skewed to drug-like space.
-- Do not deploy in clinical or regulatory settings without rigorous, domain-specific validation.
-
 ## Architecture
 - Backbone: ModernBERT
 - Hidden size: 768
@@ -289,6 +282,19 @@ Optimal parameters (per dataset) for the `MLM + DAPT + TAFT OPT` merged model:
 
 </details>
 
+## Intended Use
+* Primary: Research and development for molecular property prediction, experimentation with pooling strategies, and as a foundational model for downstream applications.
+* Appropriate for: Binary / multi-class classification (e.g., toxicity, activity) and single-task or multi-task regression (e.g., solubility, clearance) after fine-tuning.
+* Not intended for generating novel molecules.
+
+## Limitations
+- Out-of-domain performance may degrade for: very long (>128 token) SMILES, inorganic / organometallic compounds, polymers, or charged / enumerated tautomers are not well represented in training.
+- No guarantee of synthesizability, safety, or biological efficacy.
+
+## Ethical Considerations & Responsible Use
+- Potential biases arise from training corpora skewed to drug-like space.
+- Do not deploy in clinical or regulatory settings without rigorous, domain-specific validation.
+
 ## Hardware
 Training and experiments were performed on 2 NVIDIA RTX 3090 GPUs.