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import streamlit as st |
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel |
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import torch |
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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: """ |
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@st.cache(allow_output_mutation=True) |
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def get_model(): |
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model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln85Paraphrase") |
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tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln85Paraphrase") |
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tokenizer2 = AutoTokenizer.from_pretrained("gpt2") |
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model2 = AutoModelForCausalLM.from_pretrained("gpt2") |
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return model, model2, tokenizer, tokenizer2 |
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model, model2, tokenizer, tokenizer2 = get_model() |
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st.text('''For Prompt Templates: https://huggingface.co/BigSalmon/InformalToFormalLincoln82Paraphrase''') |
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temp = st.sidebar.slider("Temperature", 0.7, 1.5) |
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number_of_outputs = st.sidebar.slider("Number of Outputs", 5, 50) |
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lengths = st.sidebar.slider("Length", 3, 500) |
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bad_words = st.text_input("Words You Do Not Want Generated", " core lemon height time ") |
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logs_outputs = st.sidebar.slider("Logit Outputs", 50, 300) |
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def run_generate(text, bad_words): |
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yo = [] |
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input_ids = tokenizer.encode(text, return_tensors='pt') |
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res = len(tokenizer.encode(text)) |
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bad_words = bad_words.split() |
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bad_word_ids = [] |
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for bad_word in bad_words: |
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bad_word = " " + bad_word |
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ids = tokenizer(bad_word).input_ids |
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bad_word_ids.append(ids) |
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sample_outputs = model.generate( |
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input_ids, |
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do_sample=True, |
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max_length= res + lengths, |
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min_length = res + lengths, |
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top_k=50, |
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temperature=temp, |
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num_return_sequences=number_of_outputs, |
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bad_words_ids=bad_word_ids |
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) |
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for i in range(number_of_outputs): |
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e = tokenizer.decode(sample_outputs[i]) |
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e = e.replace(text, "") |
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yo.append(e) |
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return yo |
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def BestProbs5(prompt): |
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prompt = prompt.strip() |
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text = tokenizer.encode(prompt) |
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myinput, past_key_values = torch.tensor([text]), None |
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myinput = myinput |
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logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False) |
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logits = logits[0,-1] |
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probabilities = torch.nn.functional.softmax(logits) |
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best_logits, best_indices = logits.topk(number_of_outputs) |
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best_words = [tokenizer.decode([idx.item()]) for idx in best_indices] |
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for i in best_words[0:number_of_outputs]: |
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print("\n") |
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g = (prompt + i) |
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st.write(g) |
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l = run_generate(g, "hey") |
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st.write(l) |
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def run_generate2(text, bad_words): |
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yo = [] |
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input_ids = tokenizer2.encode(text, return_tensors='pt') |
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res = len(tokenizer2.encode(text)) |
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bad_words = bad_words.split() |
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bad_word_ids = [] |
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for bad_word in bad_words: |
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bad_word = " " + bad_word |
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ids = tokenizer2(bad_word).input_ids |
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bad_word_ids.append(ids) |
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sample_outputs = model2.generate( |
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input_ids, |
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do_sample=True, |
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max_length= res + lengths, |
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min_length = res + lengths, |
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top_k=50, |
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temperature=temp, |
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num_return_sequences=number_of_outputs, |
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bad_words_ids=bad_word_ids |
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) |
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for i in range(number_of_outputs): |
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e = tokenizer2.decode(sample_outputs[i]) |
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e = e.replace(text, "") |
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yo.append(e) |
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return yo |
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def prefix_format(sentence): |
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words = sentence.split() |
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if "[MASK]" in sentence: |
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words2 = words.index("[MASK]") |
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output = ("<Prefix> " + ' '.join(words[:words2]) + " <Prefix> " + "<Suffix> " + ' '.join(words[words2+1:]) + " <Suffix>" + " <Middle>") |
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st.write(output) |
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else: |
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st.write("Add [MASK] to sentence") |
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with st.form(key='my_form'): |
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text = st.text_area(label='Enter sentence', value=first) |
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submit_button = st.form_submit_button(label='Submit') |
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submit_button2 = st.form_submit_button(label='Submit Log Probs') |
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submit_button3 = st.form_submit_button(label='Submit Other Model') |
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submit_button4 = st.form_submit_button(label='Submit Log Probs Other Model') |
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submit_button5 = st.form_submit_button(label='Most Prob') |
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submit_button6 = st.form_submit_button(label='Turn Sentence with [MASK] into <Prefix> Format') |
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if submit_button: |
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translated_text = run_generate(text, bad_words) |
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st.write(translated_text if translated_text else "No translation found") |
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if submit_button2: |
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with torch.no_grad(): |
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text2 = str(text) |
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print(text2) |
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text3 = tokenizer.encode(text2) |
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myinput, past_key_values = torch.tensor([text3]), None |
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myinput = myinput |
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logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False) |
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logits = logits[0,-1] |
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probabilities = torch.nn.functional.softmax(logits) |
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best_logits, best_indices = logits.topk(logs_outputs) |
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best_words = [tokenizer.decode([idx.item()]) for idx in best_indices] |
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st.write(best_words) |
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if submit_button3: |
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translated_text = run_generate2(text, bad_words) |
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st.write(translated_text if translated_text else "No translation found") |
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if submit_button4: |
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text2 = str(text) |
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print(text2) |
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text3 = tokenizer2.encode(text2) |
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myinput, past_key_values = torch.tensor([text3]), None |
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myinput = myinput |
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logits, past_key_values = model2(myinput, past_key_values = past_key_values, return_dict=False) |
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logits = logits[0,-1] |
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probabilities = torch.nn.functional.softmax(logits) |
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best_logits, best_indices = logits.topk(logs_outputs) |
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best_words = [tokenizer2.decode([idx.item()]) for idx in best_indices] |
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st.write(best_words) |
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if submit_button5: |
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BestProbs5(text) |
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if submit_button6: |
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text2 = str(text) |
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prefix_format(text2) |
