add BioRedditBERT

.idea/workspace.xml

@@ -3,6 +3,7 @@
   <component name="ChangeListManager">
     <list default="true" id="2f689545-eb1b-48c0-86ea-baddfa57c626" name="Default Changelist" comment="">
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/README.md" beforeDir="false" afterPath="$PROJECT_DIR$/README.md" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/app.py" beforeDir="false" afterPath="$PROJECT_DIR$/app.py" afterDir="false" />
     </list>
     <option name="SHOW_DIALOG" value="false" />

README.md

@@ -10,3 +10,6 @@ pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+reference: https://github.com/sunil741/Medical-Chatbot-using-Bert-and-GPT2/tree/main
+

app.py

@@ -5,7 +5,7 @@ import gradio as gr
 import os
 import spaces
 from transformers import GemmaTokenizer, AutoModelForCausalLM
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, TFAutoModel
 from threading import Thread
 
 
@@ -54,13 +54,13 @@ with col1:
     st.markdown(f"[Click Here to proceed to the survey]({url})", unsafe_allow_html=True)
 
 with col2:
-    st.header("Chat with GPT")
-    # Create a container for messages
-    message_container = st.empty()
-    for message, alignment in st.session_state.chat_history:
-        align = "right" if alignment == "right" else "left"
-        st.markdown(f"<div style='text-align: {align}; color: blue;'>{message}</div>", unsafe_allow_html=True)
-    st.text_input("Ask me anything!", key="user_input", on_change=handle_send, value="")
+    # st.header("Chat with GPT")
+    # # Create a container for messages
+    # message_container = st.empty()
+    # for message, alignment in st.session_state.chat_history:
+    #     align = "right" if alignment == "right" else "left"
+    #     st.markdown(f"<div style='text-align: {align}; color: blue;'>{message}</div>", unsafe_allow_html=True)
+    # st.text_input("Ask me anything!", key="user_input", on_change=handle_send, value="")
     
     # Load the tokenizer and model
     # tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
@@ -112,4 +112,41 @@ with col2:
     #     for text in streamer:
     #         outputs.append(text)
     #         # print(outputs)
-    #         yield "".join(outputs)
+    #         yield "".join(outputs)
+    # Load tokenizer and model
+    tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/BioRedditBERT-uncased")
+    model = TFAutoModel.from_pretrained("cambridgeltl/BioRedditBERT-uncased")
+    def handle_send():
+        user_input = st.session_state.user_input
+        if user_input.strip():
+            # Encode the input
+            encoded_input = tokenizer(user_input, return_tensors='pt')
+            # Get model output
+            with torch.no_grad():
+                output = model(**encoded_input)
+    
+            # Mock response logic based on the output (you can customize this part)
+            response = "Thanks for your input. I'm still learning to chat!"
+            st.session_state.chat_history.append((user_input, "right"))
+            st.session_state.chat_history.append((response, "left"))
+            st.session_state.user_input = ""  # Clear input field after response
+    
+    # Initialize chat history if not already
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = []
+    
+    st.title("BioRedditBERT Chatbot")
+    
+    with st.container():
+        st.header("Chat with BioRedditBERT")
+        message_container = st.empty()
+        for message, alignment in st.session_state.chat_history:
+            align = "right" if alignment == "right" else "left"
+            st.markdown(f"<div style='text-align: {align}; color: blue;'>{message}</div>", unsafe_allow_html=True)
+    
+        # Text input for user input
+        user_input = st.text_input("Ask me anything!", key="user_input", on_change=handle_send, value="")
+    
+        # Button to send message
+        if st.button("Send"):
+            handle_send()

biobert-pretrained-1.1-pubmed/README.md

@@ -0,0 +1,38 @@
+# BioBERT Pre-trained Weights
+
+This repository provides pre-trained weights of BioBERT, a language representation model for biomedical domain, especially designed for biomedical text mining tasks such as biomedical named entity recognition, relation extraction, question answering, etc. Please refer to our paper [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://arxiv.org/abs/1901.08746) for more details.
+
+## Downloading pre-trained weights
+Go to [releases](https://github.com/naver/biobert-pretrained/releases) section of this repository, and download pre-trained weights of BioBERT. We provide three combinations of pre-trained weights: BioBERT (+ PubMed), BioBERT (+ PMC), and BioBERT (+ PubMed + PMC). Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google, and training details are described in our paper. Currently available versions of pre-trained weights are as follows:
+
+* **BioBERT v1.0 (+ PubMed 200K)** - based on BERT-base-Cased (same vocabulary)
+* **BioBERT v1.0 (+ PMC 270K)** - based on BERT-base-Cased (same vocabulary)
+* **BioBERT v1.0 (+ PubMed 200K + PMC 270K)** - based on BERT-base-Cased (same vocabulary)
+
+Make sure to specify the versions of pre-trained weights used in your works. Note that as we are using WordPiece vocabulary (`vocab.txt`) provided by Google, any new words in biomedical corpus can be represented with subwords (for instance, Leukemia => Leu + ##ke + ##mia). Building a new subword vocabulary for BioBERT could lose compatibility with the original pre-trained BERT. More details are in the closed [issue #1](https://github.com/naver/biobert-pretrained/issues/1).
+
+## Pre-training corpus
+We do not provide pre-processed version of each corpus. However, each pre-training corpus could be found in the following links:
+*   **`PubMed Abstracts1`**: ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/
+*   **`PubMed Abstracts2`**: ftp://ftp.ncbi.nlm.nih.gov/pubmed/updatefiles/
+*   **`PubMed Central Full Texts`**: ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/
+
+Estimated size of each corpus is 4.5 billion words for **`PubMed Abstracts1`** + **`PubMed Abstracts2`**, and 13.5 billion words for **`PubMed Central Full Texts`**.
+
+## Fine-tuning BioBERT
+To fine-tunine BioBERT on biomedical text mining tasks using provided pre-trained weights, refer to the [DMIS GitHub repository for BioBERT](https://github.com/dmis-lab/biobert).
+
+## Citation
+For now, cite [the Arxiv paper](https://arxiv.org/abs/1901.08746):
+```
+@article{lee2019biobert,
+  title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
+  author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
+  journal={arXiv preprint arXiv:1901.08746},
+  year={2019}
+}
+```
+
+## Contact information
+For help or issues using pre-trained weights of BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee
+(`lee.jnhk@gmail.com`), or Sungdong Kim (`sungdong.kim@navercorp.com`) for communication related to pre-trained weights of BioBERT.