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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel | |
| import torch | |
| first = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.\n\ninformal english: """ | |
| def get_model(): | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln2") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln21") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln40") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln49") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2InformalToFormalLincoln42") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points3") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln7") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln2") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln3") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln4") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln50") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints2") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints3") | |
| #model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m") | |
| #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln68Paraphrase") | |
| #model2 = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase") | |
| #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln73Paraphrase") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln73Paraphrase") | |
| #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln76Paraphrase") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln76Paraphrase") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln78Paraphrase") | |
| #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln78Paraphrase") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln80Paraphrase") | |
| #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln80Paraphrase") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln82Paraphrase") | |
| #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln82Paraphrase") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln83Paraphrase") | |
| #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln83Paraphrase") | |
| model = AutoModelForCausalLM.from_pretrained("cerebras/Cerebras-GPT-1.3B") | |
| #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln87Paraphrase") | |
| tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln87Paraphrase") | |
| tokenizer2 = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnMedium") | |
| model2 = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnMedium") | |
| return model, model2, tokenizer, tokenizer2 | |
| model, model2, tokenizer, tokenizer2 = get_model() | |
| st.text('''For Prompt Templates: https://huggingface.co/BigSalmon/InformalToFormalLincoln82Paraphrase''') | |
| temp = st.sidebar.slider("Temperature", 0.7, 1.5) | |
| number_of_outputs = st.sidebar.slider("Number of Outputs", 5, 50) | |
| lengths = st.sidebar.slider("Length", 3, 500) | |
| bad_words = st.text_input("Words You Do Not Want Generated", " core lemon height time ") | |
| logs_outputs = st.sidebar.slider("Logit Outputs", 50, 300) | |
| def run_generate(text, bad_words): | |
| yo = [] | |
| input_ids = tokenizer.encode(text, return_tensors='pt') | |
| res = len(tokenizer.encode(text)) | |
| bad_words = bad_words.split() | |
| bad_word_ids = [] | |
| for bad_word in bad_words: | |
| bad_word = " " + bad_word | |
| ids = tokenizer(bad_word).input_ids | |
| bad_word_ids.append(ids) | |
| sample_outputs = model.generate( | |
| input_ids, | |
| do_sample=True, | |
| max_length= res + lengths, | |
| min_length = res + lengths, | |
| top_k=50, | |
| temperature=temp, | |
| num_return_sequences=number_of_outputs, | |
| bad_words_ids=bad_word_ids | |
| ) | |
| for i in range(number_of_outputs): | |
| e = tokenizer.decode(sample_outputs[i]) | |
| e = e.replace(text, "") | |
| yo.append(e) | |
| return yo | |
| def BestProbs5(prompt): | |
| prompt = prompt.strip() | |
| text = tokenizer.encode(prompt) | |
| myinput, past_key_values = torch.tensor([text]), None | |
| myinput = myinput | |
| logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False) | |
| logits = logits[0,-1] | |
| probabilities = torch.nn.functional.softmax(logits) | |
| best_logits, best_indices = logits.topk(number_of_outputs) | |
| best_words = [tokenizer.decode([idx.item()]) for idx in best_indices] | |
| for i in best_words[0:number_of_outputs]: | |
| #print(i) | |
| print("\n") | |
| g = (prompt + i) | |
| st.write(g) | |
| l = run_generate(g, "hey") | |
| st.write(l) | |
| def run_generate2(text, bad_words): | |
| yo = [] | |
| input_ids = tokenizer2.encode(text, return_tensors='pt') | |
| res = len(tokenizer2.encode(text)) | |
| bad_words = bad_words.split() | |
| bad_word_ids = [] | |
| for bad_word in bad_words: | |
| bad_word = " " + bad_word | |
| ids = tokenizer2(bad_word).input_ids | |
| bad_word_ids.append(ids) | |
| sample_outputs = model2.generate( | |
| input_ids, | |
| do_sample=True, | |
| max_length= res + lengths, | |
| min_length = res + lengths, | |
| top_k=50, | |
| temperature=temp, | |
| num_return_sequences=number_of_outputs, | |
| bad_words_ids=bad_word_ids | |
| ) | |
| for i in range(number_of_outputs): | |
| e = tokenizer2.decode(sample_outputs[i]) | |
| e = e.replace(text, "") | |
| yo.append(e) | |
| return yo | |
| def prefix_format(sentence): | |
| words = sentence.split() | |
| if "[MASK]" in sentence: | |
| words2 = words.index("[MASK]") | |
| #print(words2) | |
| output = ("<Prefix> " + ' '.join(words[:words2]) + " <Prefix> " + "<Suffix> " + ' '.join(words[words2+1:]) + " <Suffix>" + " <Middle>") | |
| st.write(output) | |
| else: | |
| st.write("Add [MASK] to sentence") | |
| with st.form(key='my_form'): | |
| text = st.text_area(label='Enter sentence', value=first) | |
| submit_button = st.form_submit_button(label='Submit') | |
| submit_button2 = st.form_submit_button(label='Submit Log Probs') | |
| submit_button3 = st.form_submit_button(label='Submit Other Model') | |
| submit_button4 = st.form_submit_button(label='Submit Log Probs Other Model') | |
| submit_button5 = st.form_submit_button(label='Most Prob') | |
| submit_button6 = st.form_submit_button(label='Turn Sentence with [MASK] into <Prefix> Format') | |
| if submit_button: | |
| translated_text = run_generate(text, bad_words) | |
| st.write(translated_text if translated_text else "No translation found") | |
| if submit_button2: | |
| with torch.no_grad(): | |
| text2 = str(text) | |
| print(text2) | |
| text3 = tokenizer.encode(text2) | |
| myinput, past_key_values = torch.tensor([text3]), None | |
| myinput = myinput | |
| logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False) | |
| logits = logits[0,-1] | |
| probabilities = torch.nn.functional.softmax(logits) | |
| best_logits, best_indices = logits.topk(logs_outputs) | |
| best_words = [tokenizer.decode([idx.item()]) for idx in best_indices] | |
| st.write(best_words) | |
| if submit_button3: | |
| translated_text = run_generate2(text, bad_words) | |
| st.write(translated_text if translated_text else "No translation found") | |
| if submit_button4: | |
| text2 = str(text) | |
| print(text2) | |
| text3 = tokenizer2.encode(text2) | |
| myinput, past_key_values = torch.tensor([text3]), None | |
| myinput = myinput | |
| logits, past_key_values = model2(myinput, past_key_values = past_key_values, return_dict=False) | |
| logits = logits[0,-1] | |
| probabilities = torch.nn.functional.softmax(logits) | |
| best_logits, best_indices = logits.topk(logs_outputs) | |
| best_words = [tokenizer2.decode([idx.item()]) for idx in best_indices] | |
| st.write(best_words) | |
| if submit_button5: | |
| BestProbs5(text) | |
| if submit_button6: | |
| text2 = str(text) | |
| prefix_format(text2) |