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| import time | |
| import streamlit as st | |
| import string | |
| from io import StringIO | |
| import pdb | |
| import json | |
| from twc_embeddings import HFModel,SimCSEModel,SGPTModel | |
| import torch | |
| MAX_INPUT = 100 | |
| from transformers import BertTokenizer, BertForMaskedLM | |
| model_names = [ | |
| { "name":"sentence-transformers/all-MiniLM-L6-v2", | |
| "model":"sentence-transformers/all-MiniLM-L6-v2", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 3.8 million downloads from huggingface", | |
| "sota_link":"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":True, | |
| "class":"HFModel"}, | |
| { "name":"sentence-transformers/paraphrase-MiniLM-L6-v2", | |
| "model":"sentence-transformers/paraphrase-MiniLM-L6-v2", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 2.4 million downloads from huggingface", | |
| "sota_link":"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":True, | |
| "class":"HFModel"}, | |
| { "name":"sentence-transformers/bert-base-nli-mean-tokens", | |
| "model":"sentence-transformers/bert-base-nli-mean-tokens", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 700,000 downloads from huggingface", | |
| "sota_link":"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":True, | |
| "class":"HFModel"}, | |
| { "name":"sentence-transformers/all-mpnet-base-v2", | |
| "model":"sentence-transformers/all-mpnet-base-v2", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 500,000 downloads from huggingface", | |
| "sota_link":"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":True, | |
| "class":"HFModel"}, | |
| { "name":"sentence-transformers/all-MiniLM-L12-v2", | |
| "model":"sentence-transformers/all-MiniLM-L12-v2", | |
| "fork_url":"https://github.com/taskswithcode/sentence_similarity_hf_model", | |
| "orig_author_url":"https://github.com/UKPLab", | |
| "orig_author":"Ubiquitous Knowledge Processing Lab", | |
| "sota_info": { | |
| "task":"Over 500,000 downloads from huggingface", | |
| "sota_link":"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/1908.10084", | |
| "mark":True, | |
| "class":"HFModel"}, | |
| { "name":"SGPT-125M", | |
| "model":"Muennighoff/SGPT-125M-weightedmean-nli-bitfit", | |
| "fork_url":"https://github.com/taskswithcode/sgpt", | |
| "orig_author_url":"https://github.com/Muennighoff", | |
| "orig_author":"Niklas Muennighoff", | |
| "sota_info": { | |
| "task":"#1 in multiple information retrieval & search tasks(smaller variant)", | |
| "sota_link":"https://paperswithcode.com/paper/sgpt-gpt-sentence-embeddings-for-semantic", | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2202.08904v5", | |
| "mark":True, | |
| "class":"SGPTModel"}, | |
| { "name":"SGPT-1.3B", | |
| "model": "Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit", | |
| "fork_url":"https://github.com/taskswithcode/sgpt", | |
| "orig_author_url":"https://github.com/Muennighoff", | |
| "orig_author":"Niklas Muennighoff", | |
| "sota_info": { | |
| "task":"#1 in multiple information retrieval & search tasks(smaller variant)", | |
| "sota_link":"https://paperswithcode.com/paper/sgpt-gpt-sentence-embeddings-for-semantic", | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2202.08904v5", | |
| "Note":"If this large model takes too long or fails to load , try this ", | |
| "alt_url":"http://www.taskswithcode.com/sentence_similarity/", | |
| "mark":True, | |
| "class":"SGPTModel"}, | |
| { "name":"SGPT-5.8B", | |
| "model": "Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit" , | |
| "fork_url":"https://github.com/taskswithcode/sgpt", | |
| "orig_author_url":"https://github.com/Muennighoff", | |
| "orig_author":"Niklas Muennighoff", | |
| "Note":"If this large model takes too long or fails to load , try this ", | |
| "alt_url":"http://www.taskswithcode.com/sentence_similarity/", | |
| "sota_info": { | |
| "task":"#1 in multiple information retrieval & search tasks", | |
| "sota_link":"https://paperswithcode.com/paper/sgpt-gpt-sentence-embeddings-for-semantic", | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2202.08904v5", | |
| "mark":True, | |
| "class":"SGPTModel"}, | |
| { "name":"SIMCSE-large" , | |
| "model":"princeton-nlp/sup-simcse-roberta-large", | |
| "fork_url":"https://github.com/taskswithcode/SimCSE", | |
| "orig_author_url":"https://github.com/princeton-nlp", | |
| "orig_author":"Princeton Natural Language Processing", | |
| "Note":"If this large model takes too long or fails to load , try this ", | |
| "alt_url":"http://www.taskswithcode.com/sentence_similarity/", | |
| "sota_info": { | |
| "task":"Within top 10 in multiple semantic textual similarity tasks", | |
| "sota_link":"https://paperswithcode.com/paper/simcse-simple-contrastive-learning-of" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2104.08821v4", | |
| "mark":True, | |
| "class":"SimCSEModel","sota_link":"https://paperswithcode.com/sota/semantic-textual-similarity-on-sick"}, | |
| { "name":"SIMCSE-base" , | |
| "model":"princeton-nlp/sup-simcse-roberta-base", | |
| "fork_url":"https://github.com/taskswithcode/SimCSE", | |
| "orig_author_url":"https://github.com/princeton-nlp", | |
| "orig_author":"Princeton Natural Language Processing", | |
| "sota_info": { | |
| "task":"Within top 10 in multiple semantic textual similarity tasks(smaller variant)", | |
| "sota_link":"https://paperswithcode.com/paper/simcse-simple-contrastive-learning-of" | |
| }, | |
| "paper_url":"https://arxiv.org/abs/2104.08821v4", | |
| "mark":True, | |
| "class":"SimCSEModel","sota_link":"https://paperswithcode.com/sota/semantic-textual-similarity-on-sick"}, | |
| ] | |
| example_file_names = { | |
| "Machine learning terms (30+ phrases)": "small_test.txt", | |
| "Customer feedback mixed with noise (50+ sentences)":"larger_test.txt" | |
| } | |
| view_count_file = "view_count.txt" | |
| def get_views(): | |
| ret_val = 0 | |
| if ("view_count" not in st.session_state): | |
| try: | |
| data = int(open(view_count_file).read().strip("\n")) | |
| except: | |
| data = 0 | |
| data += 1 | |
| ret_val = data | |
| st.session_state["view_count"] = data | |
| with open(view_count_file,"w") as fp: | |
| fp.write(str(data)) | |
| else: | |
| ret_val = st.session_state["view_count"] | |
| return "{:,}".format(ret_val) | |
| def construct_model_info_for_display(): | |
| options_arr = [] | |
| markdown_str = f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><br/><b>Models evaluated ({len(model_names)})</b></div>" | |
| for node in model_names: | |
| options_arr .append(node["name"]) | |
| if (node["mark"] == True): | |
| markdown_str += f"<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"> • Model: <a href=\'{node['paper_url']}\' target='_blank'>{node['name']}</a><br/> Code released by: <a href=\'{node['orig_author_url']}\' target='_blank'>{node['orig_author']}</a><br/> Model info: <a href=\'{node['sota_info']['sota_link']}\' target='_blank'>{node['sota_info']['task']}</a></div>" | |
| if ("Note" in node): | |
| markdown_str += f"<div style=\"font-size:16px; color: #a91212; text-align: left\"> {node['Note']}<a href=\'{node['alt_url']}\' target='_blank'>link</a></div>" | |
| markdown_str += "<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"><br/></div>" | |
| markdown_str += "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><b>Note:</b><br/>• Uploaded files are loaded into non-persistent memory for the duration of the computation. They are not saved</div>" | |
| limit = "{:,}".format(MAX_INPUT) | |
| markdown_str += f"<div style=\"font-size:12px; color: #9f9f9f; text-align: left\">• User uploaded file has a maximum limit of {limit} sentences.</div>" | |
| return options_arr,markdown_str | |
| st.set_page_config(page_title='TWC - Compare popular/state-of-the-art models for Sentence Similarity task', page_icon="logo.jpg", layout='centered', initial_sidebar_state='auto', | |
| menu_items={ | |
| 'About': 'This app was created by taskswithcode. http://taskswithcode.com' | |
| }) | |
| col,pad = st.columns([85,15]) | |
| with col: | |
| st.image("long_form_logo_with_icon.png") | |
| def load_model(model_name): | |
| try: | |
| ret_model = None | |
| for node in model_names: | |
| if (model_name.startswith(node["name"])): | |
| obj_class = globals()[node["class"]] | |
| ret_model = obj_class() | |
| ret_model.init_model(node["model"]) | |
| assert(ret_model is not None) | |
| except Exception as e: | |
| st.error("Unable to load model:" + model_name + " " + str(e)) | |
| pass | |
| return ret_model | |
| def cached_compute_similarity(sentences,_model,model_name,main_index): | |
| texts,embeddings = _model.compute_embeddings(sentences,is_file=False) | |
| results = _model.output_results(None,texts,embeddings,main_index) | |
| return results | |
| def uncached_compute_similarity(sentences,_model,model_name,main_index): | |
| with st.spinner('Computing vectors for sentences'): | |
| texts,embeddings = _model.compute_embeddings(sentences,is_file=False) | |
| results = _model.output_results(None,texts,embeddings,main_index) | |
| #st.success("Similarity computation complete") | |
| return results | |
| def get_model_info(model_name): | |
| for node in model_names: | |
| if (model_name == node["name"]): | |
| return node | |
| def run_test(model_name,sentences,display_area,main_index,user_uploaded): | |
| display_area.text("Loading model:" + model_name) | |
| model_info = get_model_info(model_name) | |
| if ("Note" in model_info): | |
| fail_link = f"{model_info['Note']} [link]({model_info['alt_url']})" | |
| display_area.write(fail_link) | |
| model = load_model(model_name) | |
| display_area.text("Model " + model_name + " load complete") | |
| try: | |
| if (user_uploaded): | |
| results = uncached_compute_similarity(sentences,model,model_name,main_index) | |
| else: | |
| display_area.text("Computing vectors for sentences") | |
| results = cached_compute_similarity(sentences,model,model_name,main_index) | |
| display_area.text("Similarity computation complete") | |
| return results | |
| except Exception as e: | |
| st.error("Some error occurred during prediction" + str(e)) | |
| st.stop() | |
| return {} | |
| def display_results(orig_sentences,main_index,results,response_info): | |
| main_sent = f"<div style=\"font-size:14px; color: #2f2f2f; text-align: left\">{response_info}<br/><br/></div>" | |
| main_sent += "<div style=\"font-size:14px; color: #6f6f6f; text-align: left\">Results sorted by cosine distance. Closest(1) to furthest(-1) away from main sentence</div>" | |
| main_sent += f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><b>Main sentence:</b> {orig_sentences[main_index]}</div>" | |
| body_sent = [] | |
| download_data = {} | |
| for key in results: | |
| index = orig_sentences.index(key) + 1 | |
| body_sent.append(f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\">{index}] {key} <b>{results[key]:.2f}</b></div>") | |
| download_data[key] = f"{results[key]:.2f}" | |
| main_sent = main_sent + "\n" + '\n'.join(body_sent) | |
| st.markdown(main_sent,unsafe_allow_html=True) | |
| st.session_state["download_ready"] = json.dumps(download_data,indent=4) | |
| def init_session(): | |
| st.session_state["download_ready"] = None | |
| st.session_state["model_name"] = "ss_test" | |
| st.session_state["main_index"] = 1 | |
| st.session_state["file_name"] = "default" | |
| def main(): | |
| init_session() | |
| st.markdown("<h5 style='text-align: center;'>Compare popular/state-of-the-art models for Sentence Similarity task</h5>", unsafe_allow_html=True) | |
| st.markdown(f"<div style='color: #9f9f9f; text-align: right'>views: {get_views()}</div>", unsafe_allow_html=True) | |
| try: | |
| with st.form('twc_form'): | |
| uploaded_file = st.file_uploader("Step 1. Upload text file(one sentence in a line) or choose an example text file below", type=".txt") | |
| selected_file_index = st.selectbox(label='Example files ', | |
| options = list(dict.keys(example_file_names)), index=0, key = "twc_file") | |
| st.write("") | |
| options_arr,markdown_str = construct_model_info_for_display() | |
| selection_label = 'Step 2. Select Model' | |
| selected_model = st.selectbox(label=selection_label, | |
| options = options_arr, index=0, key = "twc_model") | |
| st.write("") | |
| main_index = st.number_input('Step 3. Enter index of sentence in file to make it the main sentence',value=1,min_value = 1) | |
| st.write("") | |
| submit_button = st.form_submit_button('Run') | |
| input_status_area = st.empty() | |
| display_area = st.empty() | |
| if submit_button: | |
| start = time.time() | |
| if uploaded_file is not None: | |
| st.session_state["file_name"] = uploaded_file.name | |
| sentences = StringIO(uploaded_file.getvalue().decode("utf-8")).read() | |
| else: | |
| st.session_state["file_name"] = example_file_names[selected_file_index] | |
| sentences = open(example_file_names[selected_file_index]).read() | |
| sentences = sentences.split("\n")[:-1] | |
| if (len(sentences) < main_index): | |
| main_index = len(sentences) | |
| st.info("Selected sentence index is larger than number of sentences in file. Truncating to " + str(main_index)) | |
| if (len(sentences) > MAX_INPUT): | |
| st.info(f"Input sentence count exceeds maximum sentence limit. First {MAX_INPUT} out of {len(sentences)} sentences chosen") | |
| sentences = sentences[:MAX_INPUT] | |
| st.session_state["model_name"] = selected_model | |
| st.session_state["main_index"] = main_index | |
| results = run_test(selected_model,sentences,display_area,main_index - 1,(uploaded_file is not None)) | |
| display_area.empty() | |
| with display_area.container(): | |
| device = 'GPU' if torch.cuda.is_available() else 'CPU' | |
| response_info = f"Computation time on {device}: {time.time() - start:.2f} secs for {len(sentences)} sentences" | |
| display_results(sentences,main_index - 1,results,response_info) | |
| #st.json(results) | |
| st.download_button( | |
| label="Download results as json", | |
| data= st.session_state["download_ready"] if st.session_state["download_ready"] != None else "", | |
| disabled = False if st.session_state["download_ready"] != None else True, | |
| file_name= (st.session_state["model_name"] + "_" + str(st.session_state["main_index"]) + "_" + '_'.join(st.session_state["file_name"].split(".")[:-1]) + ".json").replace("/","_"), | |
| mime='text/json', | |
| key ="download" | |
| ) | |
| except Exception as e: | |
| st.error("Some error occurred during loading" + str(e)) | |
| st.stop() | |
| st.markdown(markdown_str, unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |