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#########################################################################
# Copyright (C) #
# 2025-August Sen Li (Sen.Li.Sprout@gmail.com) #
# Permission given to modify the code only for Non-Profit Research #
# as long as you keep this declaration at the top #
#########################################################################
import os
import gradio as gr
import huggingface_hub
import sentence_transformers
from transformers import AutoTokenizer, AutoModel
import torch
# ----------------------------------------------------------------------------------------------------------------------
def func_ClearInputs(): return "", "", ""
# str_ModelID_ClinicalBERT = "medicalai/ClinicalBERT"
str_ModelID_ModernBERT = "answerdotai/ModernBERT-large"
# str_ModelID_ClinicalBERT = "TsinghuaC3I/Llama-3-8B-UltraMedical"
# tokenizer = AutoTokenizer.from_pretrained(str_ModelID_ModernBERT)
# model_ClinicalBERT = AutoModel.from_pretrained(str_ModelID_ModernBERT)
# Wrap ClinicalBERT inside SentenceTransformers
word_embedding_model = sentence_transformers.models.Transformer(str_ModelID_ModernBERT)
pooling_model = sentence_transformers.models.Pooling(word_embedding_model.get_word_embedding_dimension())
sentenceModel_ModernBERT = sentence_transformers.SentenceTransformer(modules=[word_embedding_model, pooling_model])
# device="cuda")
def get_SentenceEmbeddings_ModernBERT(sentence):
# Encode sentences in batches efficiently
embeddings = sentenceModel_ModernBERT.encode(sentence)
return embeddings
def get_sentence_embedding(sentence: str) -> torch.Tensor:
# Tokenize and encode
inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True)
# Get hidden states
with torch.no_grad():
outputs = sentenceModel_ModernBERT(**inputs)
# outputs.last_hidden_state shape: (batch_size, seq_len, hidden_dim)
token_embeddings = outputs.last_hidden_state
# Create sentence embedding (mean pooling across tokens, ignoring padding)
attention_mask = inputs['attention_mask']
mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
sentence_embedding = torch.sum(token_embeddings * mask_expanded, dim=1) / torch.clamp(mask_expanded.sum(dim=1),
min=1e-9)
return sentence_embedding.squeeze()
def func_sBERT_SimilarityResult(str_Text_1, str_Text_2):
if not str_Text_1.strip() or not str_Text_2.strip():
return "Both text inputs must be non-empty."
# 01. Load SBERT model (you can choose other pre-trained models too)
inferenceClient = huggingface_hub.InferenceClient(provider="hf-inference")
# 02. Get sentence embeddings
# str_ModelID_sBERT = "medicalai/ClinicalBERT"
# arrEmbedding_Text_1 = inferenceClient.feature_extraction(text=str_Text_1, model=str_ModelID_sBERT)
# arrEmbedding_Text_2 = inferenceClient.feature_extraction(text=str_Text_2, model=str_ModelID_sBERT)
# arrEmbedding_Text_1 = get_sentence_embedding(str_Text_1)
# arrEmbedding_Text_2 = get_sentence_embedding(str_Text_2)
arrEmbedding_Text_1 = get_SentenceEmbeddings_ModernBERT(str_Text_1)
arrEmbedding_Text_2 = get_SentenceEmbeddings_ModernBERT(str_Text_2)
# 03. Compute cosine similarity
tensor_Similarity = sentence_transformers.util.pytorch_cos_sim(arrEmbedding_Text_1, arrEmbedding_Text_2)
f_Similarity = tensor_Similarity.item()
return f"Clinical Similarity Score: {f_Similarity:.4f}"
# ----------------------------------------------------------------------------------------------------------------------
# Launch the interface and MCP server
if __name__ == "__main__":
print(f"os.getcwd() = {os.getcwd()}")
os.system(f"echo ls -al {os.getcwd()} && ls -al {os.getcwd()}")
os.system(f"echo ls -al /: && ls -al /")
os.system(f"echo ls -al /home/: && ls -al /home/")
# 03. Gradio UI elements
with gr.Blocks() as grBlocks_SentenceSimilarity__MCP_Server:
gr.Markdown("# ModernBERT for Clinical Text Similarity using HF Inference Server, MaxSeqLength==8192")
gr.Markdown("This application calculates Cosine Similarity Score between two Texts' ModernBERT Sentence-Embeddings")
with gr.Row():
grTextBox_Input_1 = gr.Textbox(label="Text Panel 1", lines=20)
grTextBox_Input_2 = gr.Textbox(label="Text Panel 2", lines=20)
with gr.Row():
with gr.Column(scale=1):
grButton_Clear = gr.Button("Clear")
grButton_Submit = gr.Button("Submit")
with gr.Column(scale=3):
grTextbox_Output = gr.Textbox(label="Similarity Result", interactive=False)
# Set button functionality
grButton_Submit.click(fn=func_sBERT_SimilarityResult, inputs=[grTextBox_Input_1, grTextBox_Input_2], outputs=grTextbox_Output)
grButton_Clear.click(fn=func_ClearInputs, inputs=[], outputs=[grTextBox_Input_1, grTextBox_Input_2, grTextbox_Output])
# 04. Launch Gradio MCP server
grBlocks_SentenceSimilarity__MCP_Server.launch(mcp_server=True, share=True) |