File size: 9,401 Bytes
068768a 12b2310 068768a 2c25e3b 12b2310 1a8295f 068768a 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 ba87784 d54493e 12b2310 068768a 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 068768a 12b2310 068768a 12b2310 068768a 12b2310 068768a 12b2310 068768a 12b2310 2c25e3b 12b2310 2c25e3b 12b2310 e97e752 12b2310 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
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: """
@st.cache(allow_output_mutation=True)
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/InformalToFormalLincoln85Paraphrase")
#tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln85Paraphrase")
#model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln88Paraphrase")
#tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln88Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase")
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase")
tokenizer2 = AutoTokenizer.from_pretrained("gpt2")
model2 = AutoModelForCausalLM.from_pretrained("gpt2")
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) |