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446ee68 e978111 446ee68 e978111 3822e0d 446ee68 f75d7b7 446ee68 f75d7b7 446ee68 f75d7b7 446ee68 4e465dd 446ee68 f75d7b7 446ee68 f75d7b7 446ee68 3822e0d 6be5732 9d10ce7 3822e0d 6be5732 3822e0d 6be5732 3822e0d 6be5732 3822e0d 446ee68 | 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 | import numpy as np
import pandas as pd
import random
import solara
import torch
import torch.nn.functional as F
import ipyvue
import reacton
from solara.alias import rv as v
from typing import Any, Callable, Optional, TypeVar, Union, cast, overload
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('gpt2', padding_side='left')
model = AutoModelForCausalLM.from_pretrained('gpt2')
def use_change(el: reacton.core.Element, on_value: Callable[[Any], Any], enabled=True):
"""Trigger a callback when a blur events occurs or the enter key is pressed."""
on_value_ref = solara.use_ref(on_value)
on_value_ref.current = on_value
def add_events():
def on_change(widget, event, data):
if enabled:
on_value_ref.current(widget.v_model)
widget = cast(ipyvue.VueWidget, solara.get_widget(el))
if enabled:
widget.on_event("blur", on_change)
widget.on_event("keyup.enter", on_change)
def cleanup():
if enabled:
widget.on_event("blur", on_change, remove=True)
widget.on_event("keyup.enter", on_change, remove=True)
return cleanup
solara.use_effect(add_events, [enabled])
@solara.component
def InputTextarea(
label: str,
value: Union[str, solara.Reactive[str]] = "",
on_value: Callable[[str], None] = None,
disabled: bool = False,
password: bool = False,
continuous_update: bool = False,
error: Union[bool, str] = False,
message: Optional[str] = None,
):
reactive_value = solara.use_reactive(value, on_value)
del value, on_value
def set_value_cast(value):
reactive_value.value = str(value)
def on_v_model(value):
if continuous_update:
set_value_cast(value)
messages = []
if error and isinstance(error, str):
messages.append(error)
elif message:
messages.append(message)
text_area = v.Textarea(
v_model=reactive_value.value,
on_v_model=on_v_model,
label=label,
disabled=disabled,
type="password" if password else None,
error=bool(error),
messages=messages,
solo=True,
hide_details=True,
outlined=True,
rows=1,
auto_grow=True,
)
use_change(text_area, set_value_cast, enabled=not continuous_update)
return text_area
css = """
.mystronggreen{
background-color:#99ff99;
color:black!important;
padding:0px;
white-space-collapse:preserve;
}
.mygreen{
background-color:#ccffcc;
color:black!important;
white-space-collapse:preserve;
}
.myyellow{
background-color: #ffff99;
color:black!important;
white-space-collapse:preserve;
}
.myorange{
background-color: #ffe6cc;
color:black!important;
white-space-collapse:preserve;
}
.myred{
background-color:#ffcab0;
color:black!important;
white-space-collapse:preserve;
}
"""
def get_color(diff):
if np.abs(diff)<1:
color = "mystronggreen"
elif np.abs(diff)<10:
color = "mygreen"
elif np.abs(diff)<20:
color = "myyellow"
elif np.abs(diff)<30:
color = "myorange"
else:
color = "myred"
return color
text1 = solara.reactive("""If I start counting to ten...
one two three four five six seven eight nine ten
... you can see that as the sequence progresses, the LM gets better at predicting the sequence and by the end, it's 100% correct about how things are going to continue.
The first time I write this sentence, the model is quite confused about what token is about to come next, especially if I throw in weird words like pumpkin, clown, tweets, alpha, teddy bear.
But the second time? It expects almost every word that appears:
The first time I write this sentence, the model is quite confused about what token is about to come next, especially if I throw in weird words like pumpkin, clown, tweets, alpha, teddy bear.""")
@solara.component
def Page():
with solara.Column(margin="10"):
solara.Markdown("#Perplexity")
solara.Markdown("This is an educational tool. For any given passage of text, this tool augments the original text with highlights and annotations that indicate how 'surprising' each token is to the model, as well as which other tokens the model deemed most likely to occur in its place when you hover over each token.")
InputTextarea("Enter text:", value=text1, continuous_update=True)
if text1.value != "":
tokens = tokenizer.encode(text1.value, return_tensors="pt")
full_list = []
partial_list = []
for token in tokens[0]:
if token != 198:
partial_list.append(token)
else:
partial_list.append(torch.tensor(198))
full_list.append(partial_list)
partial_list = []
if len(partial_list) != 0:
full_list.append(partial_list)
tokens = torch.cat((torch.tensor([tokenizer.eos_token_id]), tokens[0])).reshape(1,-1)
i = 0
for j in range(len(full_list)):
with solara.Column():
with solara.Div(style="display: inline;"):
for k in range(len(full_list[j])):
outputs = model.generate(tokens[0][:i+1].reshape(1,-1), max_new_tokens=1, output_scores=True, return_dict_in_generate=True, pad_token_id=tokenizer.eos_token_id)
scores = F.softmax(outputs.scores[0], dim=-1)
top_10 = torch.topk(scores, 10)
df = pd.DataFrame()
a = scores[0][tokens[0][i+1]]
b = top_10.values
c = torch.concat([a.reshape(-1,1)[0], b[0]])
df["probs"] = list([c[i].cpu().item() for i in range(len(c))])
diff = 100*(df["probs"].iloc[0]-df["probs"].iloc[1])
color = get_color(diff)
df["probs"] = [f"{value:.2%}" for value in df["probs"].values]
aux = [tokenizer.decode(tokens[0][i+1]).replace(" ", "␣")] + [tokenizer.decode(top_10.indices[0][i]).replace(" ", "␣") for i in range(10)]
df["predicted next token"] = aux
solara_df = solara.DataFrame(df, items_per_page=11)
with solara.Tooltip(solara_df, color="white"):
solara.Style(css)
if full_list[j][k] == 198:
solara.Text("↵", classes=[f"{color}"])
else:
solara.Text(f"{tokenizer.decode(full_list[j][k])}", classes=[f"{color}"])
i+=1
Page()
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