Spaces:
Sleeping
Sleeping
Mila
commited on
Commit
•
3139db4
1
Parent(s):
33e257e
This time for sure x4
Browse files- app_context.py +253 -257
- flan-t5-train.py +234 -301
- results/checkpoint-16000/added_tokens.json +102 -0
- results/checkpoint-16000/config.json +62 -0
- results/checkpoint-16000/generation_config.json +6 -0
- results/checkpoint-16000/model.safetensors +3 -0
- results/checkpoint-16000/optimizer.pt +3 -0
- results/checkpoint-16000/rng_state.pth +3 -0
- results/checkpoint-16000/scheduler.pt +3 -0
- results/checkpoint-16000/special_tokens_map.json +125 -0
- results/checkpoint-16000/spiece.model +3 -0
- results/checkpoint-16000/tokenizer_config.json +939 -0
- results/checkpoint-16000/trainer_state.json +319 -0
- results/checkpoint-16000/training_args.bin +3 -0
- results/checkpoint-16500/added_tokens.json +102 -0
- results/checkpoint-16500/config.json +62 -0
- results/checkpoint-16500/generation_config.json +6 -0
- results/checkpoint-16500/model.safetensors +3 -0
- results/checkpoint-16500/optimizer.pt +3 -0
- results/checkpoint-16500/rng_state.pth +3 -0
- results/checkpoint-16500/scheduler.pt +3 -0
- results/checkpoint-16500/special_tokens_map.json +125 -0
- results/checkpoint-16500/spiece.model +3 -0
- results/checkpoint-16500/tokenizer_config.json +939 -0
- results/checkpoint-16500/trainer_state.json +325 -0
- results/checkpoint-16500/training_args.bin +3 -0
- results/checkpoint-17000/added_tokens.json +102 -0
- results/checkpoint-17000/config.json +62 -0
- results/checkpoint-17000/generation_config.json +6 -0
- results/checkpoint-17000/model.safetensors +3 -0
- results/checkpoint-17000/optimizer.pt +3 -0
- results/checkpoint-17000/rng_state.pth +3 -0
- results/checkpoint-17000/scheduler.pt +3 -0
- results/checkpoint-17000/special_tokens_map.json +125 -0
- results/checkpoint-17000/spiece.model +3 -0
- results/checkpoint-17000/tokenizer_config.json +939 -0
- results/checkpoint-17000/trainer_state.json +331 -0
- results/checkpoint-17000/training_args.bin +3 -0
- word_embedding.py +619 -0
app_context.py
CHANGED
@@ -1,258 +1,254 @@
|
|
1 |
-
import gradio as gr
|
2 |
-
import math
|
3 |
-
import spacy
|
4 |
-
from datasets import load_dataset
|
5 |
-
from
|
6 |
-
from
|
7 |
-
from
|
8 |
-
|
9 |
-
|
10 |
-
from
|
11 |
-
|
12 |
-
import
|
13 |
-
import
|
14 |
-
from
|
15 |
-
import
|
16 |
-
import
|
17 |
-
import
|
18 |
-
from
|
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 |
-
print(inputs)
|
119 |
-
|
120 |
-
|
121 |
-
|
122 |
-
|
123 |
-
|
124 |
-
|
125 |
-
|
126 |
-
|
127 |
-
|
128 |
-
|
129 |
-
|
130 |
-
|
131 |
-
|
132 |
-
|
133 |
-
|
134 |
-
|
135 |
-
|
136 |
-
|
137 |
-
|
138 |
-
|
139 |
-
|
140 |
-
#
|
141 |
-
line
|
142 |
-
|
143 |
-
|
144 |
-
|
145 |
-
|
146 |
-
|
147 |
-
|
148 |
-
|
149 |
-
|
150 |
-
|
151 |
-
|
152 |
-
|
153 |
-
|
154 |
-
|
155 |
-
#
|
156 |
-
|
157 |
-
|
158 |
-
|
159 |
-
|
160 |
-
|
161 |
-
|
162 |
-
|
163 |
-
|
164 |
-
|
165 |
-
global
|
166 |
-
|
167 |
-
|
168 |
-
|
169 |
-
|
170 |
-
|
171 |
-
|
172 |
-
|
173 |
-
|
174 |
-
|
175 |
-
|
176 |
-
|
177 |
-
|
178 |
-
|
179 |
-
|
180 |
-
|
181 |
-
|
182 |
-
|
183 |
-
|
184 |
-
|
185 |
-
|
186 |
-
|
187 |
-
global
|
188 |
-
global
|
189 |
-
|
190 |
-
|
191 |
-
|
192 |
-
|
193 |
-
|
194 |
-
|
195 |
-
|
196 |
-
|
197 |
-
|
198 |
-
|
199 |
-
|
200 |
-
|
201 |
-
|
202 |
-
|
203 |
-
|
204 |
-
|
205 |
-
|
206 |
-
|
207 |
-
|
208 |
-
|
209 |
-
|
210 |
-
|
211 |
-
|
212 |
-
|
213 |
-
|
214 |
-
|
215 |
-
|
216 |
-
|
217 |
-
|
218 |
-
|
219 |
-
|
220 |
-
|
221 |
-
|
222 |
-
|
223 |
-
|
224 |
-
global
|
225 |
-
|
226 |
-
|
227 |
-
#
|
228 |
-
|
229 |
-
|
230 |
-
|
231 |
-
|
232 |
-
|
233 |
-
|
234 |
-
|
235 |
-
|
236 |
-
|
237 |
-
|
238 |
-
|
239 |
-
|
240 |
-
|
241 |
-
with gr.
|
242 |
-
|
243 |
-
|
244 |
-
|
245 |
-
|
246 |
-
|
247 |
-
|
248 |
-
|
249 |
-
|
250 |
-
|
251 |
-
|
252 |
-
|
253 |
-
|
254 |
-
|
255 |
-
|
256 |
-
|
257 |
-
if __name__ == "__main__":
|
258 |
main()
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import math
|
3 |
+
import spacy
|
4 |
+
from datasets import load_dataset
|
5 |
+
from transformers import pipeline, T5Tokenizer
|
6 |
+
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
|
7 |
+
from transformers import TrainingArguments, Trainer, T5ForConditionalGeneration
|
8 |
+
import torch
|
9 |
+
import torch.nn.functional as F
|
10 |
+
from torch.utils.data import DataLoader
|
11 |
+
import numpy as np
|
12 |
+
import evaluate
|
13 |
+
import nltk
|
14 |
+
from nltk.corpus import stopwords
|
15 |
+
import subprocess
|
16 |
+
import sys
|
17 |
+
import random
|
18 |
+
from textwrap import fill
|
19 |
+
|
20 |
+
# !pip install https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl
|
21 |
+
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl'])
|
22 |
+
# tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
|
23 |
+
model_base = "results/checkpoint-17000"
|
24 |
+
nltk.download('stopwords')
|
25 |
+
nlp = spacy.load("en_core_web_sm")
|
26 |
+
stops = stopwords.words("english")
|
27 |
+
ROMAN_CONSTANTS = (
|
28 |
+
( "", "I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX" ),
|
29 |
+
( "", "X", "XX", "XXX", "XL", "L", "LX", "LXX", "LXXX", "XC" ),
|
30 |
+
( "", "C", "CC", "CCC", "CD", "D", "DC", "DCC", "DCCC", "CM" ),
|
31 |
+
( "", "M", "MM", "MMM", "", "", "-", "", "", "" ),
|
32 |
+
( "", "i", "ii", "iii", "iv", "v", "vi", "vii", "viii", "ix" ),
|
33 |
+
( "", "x", "xx", "xxx", "xl", "l", "lx", "lxx", "lxxx", "xc" ),
|
34 |
+
( "", "c", "cc", "ccc", "cd", "d", "dc", "dcc", "dccc", "cm" ),
|
35 |
+
( "", "m", "mm", "mmm", "", "", "-", "", "", "" ),
|
36 |
+
)
|
37 |
+
|
38 |
+
# answer = "Pizza"
|
39 |
+
guesses = []
|
40 |
+
return_guesses = []
|
41 |
+
answer = "Moon"
|
42 |
+
word1 = "Black"
|
43 |
+
word2 = "White"
|
44 |
+
word3 = "Sun"
|
45 |
+
base_prompts = ["Sun is to Moon as ", "Black is to White as ", "Atom is to Element as",
|
46 |
+
"Athens is to Greece as ", "Cat is to Dog as ", "Robin is to Bird as",
|
47 |
+
"Hunger is to Ambition as "]
|
48 |
+
|
49 |
+
|
50 |
+
#Mean Pooling - Take attention mask into account for correct averaging
|
51 |
+
def mean_pooling(model_output, attention_mask):
|
52 |
+
token_embeddings = model_output['token_embeddings'] #First element of model_output contains all token embeddings
|
53 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
54 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
55 |
+
|
56 |
+
|
57 |
+
def normalize(comment, lowercase, remove_stopwords):
|
58 |
+
if lowercase:
|
59 |
+
comment = comment.lower()
|
60 |
+
comment = nlp(comment)
|
61 |
+
lemmatized = list()
|
62 |
+
for word in comment:
|
63 |
+
lemma = word.lemma_.strip()
|
64 |
+
if lemma:
|
65 |
+
if not remove_stopwords or (remove_stopwords and lemma not in stops):
|
66 |
+
lemmatized.append(lemma)
|
67 |
+
return " ".join(lemmatized)
|
68 |
+
|
69 |
+
|
70 |
+
# def tokenize_function(examples):
|
71 |
+
# return tokenizer(examples["text"])
|
72 |
+
|
73 |
+
|
74 |
+
def compute_metrics(eval_pred):
|
75 |
+
logits, labels = eval_pred
|
76 |
+
predictions = np.argmax(logits, axis=-1)
|
77 |
+
metric = evaluate.load("accuracy")
|
78 |
+
return metric.compute(predictions=predictions, references=labels)
|
79 |
+
|
80 |
+
|
81 |
+
def get_model():
|
82 |
+
global model_base
|
83 |
+
# last_checkpoint = "./results/checkpoint-22500"
|
84 |
+
|
85 |
+
finetuned_model = T5ForConditionalGeneration.from_pretrained(model_base)
|
86 |
+
tokenizer = T5Tokenizer.from_pretrained(model_base)
|
87 |
+
# model = SentenceTransformer(model_base)
|
88 |
+
gpu_available = torch.cuda.is_available()
|
89 |
+
device = torch.device("cuda" if gpu_available else "cpu")
|
90 |
+
finetuned_model = finetuned_model.to(device)
|
91 |
+
return finetuned_model, tokenizer
|
92 |
+
|
93 |
+
|
94 |
+
def cosine_scores(model, sentence):
|
95 |
+
global word1
|
96 |
+
global word2
|
97 |
+
global word3
|
98 |
+
# sentence1 = f"{word1} is to {word2} as"
|
99 |
+
embeddings1 = model.encode(sentence, convert_to_tensor=True)
|
100 |
+
|
101 |
+
def embeddings(model, sentences, tokenizer):
|
102 |
+
global word1
|
103 |
+
global word2
|
104 |
+
global word3
|
105 |
+
global model_base
|
106 |
+
gpu_available = torch.cuda.is_available()
|
107 |
+
device = torch.device("cuda" if gpu_available else "cpu")
|
108 |
+
# device = torch.device('cuda:0')
|
109 |
+
# embeddings = model.encode(sentences)
|
110 |
+
question = "Please answer to this question: " + sentences
|
111 |
+
|
112 |
+
inputs = tokenizer(question, return_tensors="pt")
|
113 |
+
|
114 |
+
print(inputs)
|
115 |
+
# print(inputs.device)
|
116 |
+
print(model.device)
|
117 |
+
print(inputs['input_ids'].device)
|
118 |
+
print(inputs['attention_mask'].device)
|
119 |
+
|
120 |
+
inputs['attention_mask'] = inputs['attention_mask'].to(device)
|
121 |
+
inputs['input_ids'] = inputs['input_ids'].to(device)
|
122 |
+
|
123 |
+
outputs = model.generate(**inputs)
|
124 |
+
answer = tokenizer.decode(outputs[0])
|
125 |
+
answer = answer[6:-4]
|
126 |
+
# print(fill(answer, width=80))
|
127 |
+
|
128 |
+
print("ANSWER IS", answer)
|
129 |
+
|
130 |
+
return answer
|
131 |
+
|
132 |
+
|
133 |
+
def random_word(model, tokenizer):
|
134 |
+
global model_base
|
135 |
+
vocab = tokenizer.get_vocab()
|
136 |
+
# with open(model_base + '/vocab.txt', 'r') as file:
|
137 |
+
line = ""
|
138 |
+
# content = file.readlines()
|
139 |
+
length = tokenizer.vocab_size
|
140 |
+
# print(vocab)
|
141 |
+
while line == "":
|
142 |
+
rand_line = random.randrange(0, length)
|
143 |
+
# print("TRYING TO FIND", rand_line, "OUT OF", length, "WITH VOCAB OF TYPE", type(vocab))
|
144 |
+
for word, id in vocab.items():
|
145 |
+
if id == rand_line and word[0].isalpha() and word not in stops and word not in ROMAN_CONSTANTS:
|
146 |
+
# if vocab[rand_line][0].isalpha() and vocab[rand_line][:-1] not in stops and vocab[rand_line][:-1] not in ROMAN_CONSTANTS:
|
147 |
+
line = word
|
148 |
+
elif id == rand_line:
|
149 |
+
print(f"{word} is not alpha or is a stop word")
|
150 |
+
# for num, aline in enumerate(file, 1997):
|
151 |
+
# if random.randrange(num) and aline.isalpha():
|
152 |
+
# continue
|
153 |
+
# # elif not aline.isalpha():
|
154 |
+
|
155 |
+
# line = aline
|
156 |
+
print(line)
|
157 |
+
return line
|
158 |
+
|
159 |
+
|
160 |
+
def generate_prompt(model, tokenizer):
|
161 |
+
global word1
|
162 |
+
global word2
|
163 |
+
global word3
|
164 |
+
global answer
|
165 |
+
global base_prompts
|
166 |
+
word1 = random_word(model, tokenizer)
|
167 |
+
# word2 = random_word()
|
168 |
+
|
169 |
+
word2 = embeddings(model, f"{base_prompts[random.randint(0, len(base_prompts) - 1)]}{word1} is to ___.", tokenizer)
|
170 |
+
word3 = random_word(model, tokenizer)
|
171 |
+
sentence = f"{word1} is to {word2} as {word3} is to ___."
|
172 |
+
print(sentence)
|
173 |
+
answer = embeddings(model, sentence, tokenizer)
|
174 |
+
print("ANSWER IS", answer)
|
175 |
+
return f"# {word1} is to {word2} as {word3} is to ___."
|
176 |
+
# cosine_scores(model, sentence)
|
177 |
+
|
178 |
+
|
179 |
+
def greet(name):
|
180 |
+
return "Hello " + name + "!!"
|
181 |
+
|
182 |
+
def check_answer(guess:str):
|
183 |
+
global guesses
|
184 |
+
global answer
|
185 |
+
global return_guesses
|
186 |
+
global word1
|
187 |
+
global word2
|
188 |
+
global word3
|
189 |
+
|
190 |
+
model, tokenizer = get_model()
|
191 |
+
output = ""
|
192 |
+
protected_guess = guess
|
193 |
+
sentence = f"{word1} is to {word2} as [MASK] is to {guess}."
|
194 |
+
|
195 |
+
other_word = embeddings(model, sentence, tokenizer)
|
196 |
+
guesses.append(guess)
|
197 |
+
|
198 |
+
|
199 |
+
|
200 |
+
for guess in return_guesses:
|
201 |
+
output += ("- " + guess + "<br>")
|
202 |
+
|
203 |
+
# output = output[:-1]
|
204 |
+
prompt = f"{word1} is to {word2} as {word3} is to ___."
|
205 |
+
# print("IS", protected_guess, "EQUAL TO", answer, ":", protected_guess.lower() == answer.lower())
|
206 |
+
|
207 |
+
if protected_guess.lower() == answer.lower():
|
208 |
+
return_guesses.append(f"{protected_guess}: {word1} is to {word2} as {word3} is to {protected_guess}.")
|
209 |
+
output += f"<span style='color:green'>- {return_guesses[-1]}</span><br>"
|
210 |
+
new_prompt = generate_prompt(model, tokenizer)
|
211 |
+
return new_prompt, "Correct!", output
|
212 |
+
else:
|
213 |
+
return_guess = f"{protected_guess}: {word1} is to {word2} as {other_word} is to {protected_guess}."
|
214 |
+
return_guesses.append(return_guess)
|
215 |
+
output += ("- " + return_guess + " <br>")
|
216 |
+
return prompt, "Try again!", output
|
217 |
+
|
218 |
+
def main():
|
219 |
+
global word1
|
220 |
+
global word2
|
221 |
+
global word3
|
222 |
+
global answer
|
223 |
+
# answer = "Moon"
|
224 |
+
global guesses
|
225 |
+
|
226 |
+
|
227 |
+
# num_rows, data_type, value, example, embeddings = training()
|
228 |
+
# sent_embeddings = embeddings()
|
229 |
+
model, tokenizer = get_model()
|
230 |
+
generate_prompt(model, tokenizer)
|
231 |
+
|
232 |
+
prompt = f"{word1} is to {word2} as {word3} is to ____"
|
233 |
+
print(prompt)
|
234 |
+
print("TESTING EMBEDDINGS")
|
235 |
+
with gr.Blocks() as iface:
|
236 |
+
mark_question = gr.Markdown(prompt)
|
237 |
+
with gr.Tab("Guess"):
|
238 |
+
text_input = gr.Textbox()
|
239 |
+
text_output = gr.Textbox()
|
240 |
+
text_button = gr.Button("Submit")
|
241 |
+
with gr.Accordion("Open for previous guesses"):
|
242 |
+
text_guesses = gr.Markdown()
|
243 |
+
# with gr.Tab("Testing"):
|
244 |
+
# gr.Markdown(f"""The Embeddings are {sent_embeddings}.""")
|
245 |
+
text_button.click(check_answer, inputs=[text_input], outputs=[mark_question, text_output, text_guesses])
|
246 |
+
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
|
247 |
+
iface.launch()
|
248 |
+
|
249 |
+
|
250 |
+
|
251 |
+
|
252 |
+
|
253 |
+
if __name__ == "__main__":
|
|
|
|
|
|
|
|
|
254 |
main()
|
flan-t5-train.py
CHANGED
@@ -1,302 +1,235 @@
|
|
1 |
-
import gradio as gr
|
2 |
-
import math
|
3 |
-
from datasets import load_dataset
|
4 |
-
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
|
5 |
-
from transformers import TrainingArguments, Trainer
|
6 |
-
from transformers import T5Tokenizer, T5ForConditionalGeneration
|
7 |
-
import torch
|
8 |
-
import torch.nn.functional as F
|
9 |
-
from torch.utils.data import DataLoader
|
10 |
-
import numpy as np
|
11 |
-
import evaluate
|
12 |
-
import nltk
|
13 |
-
from nltk.corpus import stopwords
|
14 |
-
import subprocess
|
15 |
-
import sys
|
16 |
-
from transformers import T5Tokenizer, DataCollatorForSeq2Seq
|
17 |
-
from transformers import T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer
|
18 |
-
from transformers import DataCollatorWithPadding, DistilBertTokenizerFast
|
19 |
-
from transformers import TrainingArguments
|
20 |
-
from transformers import (
|
21 |
-
BertModel,
|
22 |
-
BertTokenizerFast,
|
23 |
-
Trainer,
|
24 |
-
EvalPrediction
|
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 |
-
return
|
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 |
-
print("
|
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 |
-
|
190 |
-
|
191 |
-
|
192 |
-
#
|
193 |
-
|
194 |
-
|
195 |
-
|
196 |
-
|
197 |
-
|
198 |
-
#
|
199 |
-
|
200 |
-
|
201 |
-
|
202 |
-
|
203 |
-
|
204 |
-
|
205 |
-
|
206 |
-
|
207 |
-
|
208 |
-
|
209 |
-
|
210 |
-
|
211 |
-
|
212 |
-
|
213 |
-
|
214 |
-
|
215 |
-
|
216 |
-
|
217 |
-
|
218 |
-
|
219 |
-
|
220 |
-
|
221 |
-
|
222 |
-
|
223 |
-
|
224 |
-
|
225 |
-
|
226 |
-
|
227 |
-
|
228 |
-
|
229 |
-
|
230 |
-
|
231 |
-
|
232 |
-
|
233 |
-
|
234 |
-
|
235 |
-
eval_dataset=dataset["test"],
|
236 |
-
# evaluation_strategy="no"
|
237 |
-
tokenizer=tokenizer,
|
238 |
-
data_collator=data_collator,
|
239 |
-
compute_metrics=compute_metrics
|
240 |
-
)
|
241 |
-
|
242 |
-
# model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=10)
|
243 |
-
|
244 |
-
trainer.train()
|
245 |
-
|
246 |
-
# model.save("flan-analogies")
|
247 |
-
|
248 |
-
# model.save_to_hub("smhavens/bert-base-analogies")
|
249 |
-
# accuracy = compute_metrics(eval, metric)
|
250 |
-
return 0
|
251 |
-
|
252 |
-
def greet(name):
|
253 |
-
return "Hello " + name + "!!"
|
254 |
-
|
255 |
-
def check_answer(guess:str):
|
256 |
-
global guesses
|
257 |
-
global answer
|
258 |
-
guesses.append(guess)
|
259 |
-
output = ""
|
260 |
-
for guess in guesses:
|
261 |
-
output += ("- " + guess + "\n")
|
262 |
-
output = output[:-1]
|
263 |
-
|
264 |
-
if guess.lower() == answer.lower():
|
265 |
-
return "Correct!", output
|
266 |
-
else:
|
267 |
-
return "Try again!", output
|
268 |
-
|
269 |
-
def main():
|
270 |
-
print("BEGIN")
|
271 |
-
word1 = "Black"
|
272 |
-
word2 = "White"
|
273 |
-
word3 = "Sun"
|
274 |
-
global answer
|
275 |
-
answer = "Moon"
|
276 |
-
global guesses
|
277 |
-
|
278 |
-
training()
|
279 |
-
|
280 |
-
# prompt = f"{word1} is to {word2} as {word3} is to ____"
|
281 |
-
# with gr.Blocks() as iface:
|
282 |
-
# gr.Markdown(prompt)
|
283 |
-
# with gr.Tab("Guess"):
|
284 |
-
# text_input = gr.Textbox()
|
285 |
-
# text_output = gr.Textbox()
|
286 |
-
# text_button = gr.Button("Submit")
|
287 |
-
# with gr.Accordion("Open for previous guesses"):
|
288 |
-
# text_guesses = gr.Textbox()
|
289 |
-
# with gr.Tab("Testing"):
|
290 |
-
# gr.Markdown(f"""Number of rows in dataset is {num_rows}, with each having type {data_type} and value {value}.
|
291 |
-
# An example is {example}.
|
292 |
-
# The Embeddings are {embeddings}.""")
|
293 |
-
# text_button.click(check_answer, inputs=[text_input], outputs=[text_output, text_guesses])
|
294 |
-
# # iface = gr.Interface(fn=greet, inputs="text", outputs="text")
|
295 |
-
# iface.launch()
|
296 |
-
|
297 |
-
|
298 |
-
|
299 |
-
|
300 |
-
|
301 |
-
if __name__ == "__main__":
|
302 |
main()
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import math
|
3 |
+
from datasets import load_dataset
|
4 |
+
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
|
5 |
+
from transformers import TrainingArguments, Trainer
|
6 |
+
from transformers import T5Tokenizer, T5ForConditionalGeneration
|
7 |
+
import torch
|
8 |
+
import torch.nn.functional as F
|
9 |
+
from torch.utils.data import DataLoader
|
10 |
+
import numpy as np
|
11 |
+
import evaluate
|
12 |
+
import nltk
|
13 |
+
from nltk.corpus import stopwords
|
14 |
+
import subprocess
|
15 |
+
import sys
|
16 |
+
from transformers import T5Tokenizer, DataCollatorForSeq2Seq
|
17 |
+
from transformers import T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer
|
18 |
+
from transformers import DataCollatorWithPadding, DistilBertTokenizerFast
|
19 |
+
from transformers import TrainingArguments
|
20 |
+
from transformers import (
|
21 |
+
BertModel,
|
22 |
+
BertTokenizerFast,
|
23 |
+
Trainer,
|
24 |
+
EvalPrediction
|
25 |
+
)
|
26 |
+
|
27 |
+
nltk.download("punkt", quiet=True)
|
28 |
+
metric = evaluate.load("rouge")
|
29 |
+
|
30 |
+
# Global Parameters
|
31 |
+
L_RATE = 3e-4
|
32 |
+
BATCH_SIZE = 8
|
33 |
+
PER_DEVICE_EVAL_BATCH = 4
|
34 |
+
WEIGHT_DECAY = 0.01
|
35 |
+
SAVE_TOTAL_LIM = 3
|
36 |
+
NUM_EPOCHS = 10
|
37 |
+
|
38 |
+
# Set up training arguments
|
39 |
+
training_args = Seq2SeqTrainingArguments(
|
40 |
+
output_dir="./results",
|
41 |
+
evaluation_strategy="epoch",
|
42 |
+
learning_rate=L_RATE,
|
43 |
+
per_device_train_batch_size=BATCH_SIZE,
|
44 |
+
per_device_eval_batch_size=PER_DEVICE_EVAL_BATCH,
|
45 |
+
weight_decay=WEIGHT_DECAY,
|
46 |
+
save_total_limit=SAVE_TOTAL_LIM,
|
47 |
+
num_train_epochs=NUM_EPOCHS,
|
48 |
+
predict_with_generate=True,
|
49 |
+
push_to_hub=False
|
50 |
+
)
|
51 |
+
|
52 |
+
model_id = "google/flan-t5-base"
|
53 |
+
tokenizer = T5Tokenizer.from_pretrained(model_id)
|
54 |
+
# tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
|
55 |
+
# metric = evaluate.load("accuracy")
|
56 |
+
|
57 |
+
def tokenize_function(examples):
|
58 |
+
return tokenizer(examples["stem"], padding="max_length", truncation=True)
|
59 |
+
|
60 |
+
|
61 |
+
#Mean Pooling - Take attention mask into account for correct averaging
|
62 |
+
def mean_pooling(model_output, attention_mask):
|
63 |
+
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
|
64 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
65 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
66 |
+
|
67 |
+
|
68 |
+
# def compute_metrics(eval_pred):
|
69 |
+
# logits, labels = eval_pred
|
70 |
+
# predictions = np.argmax(logits, axis=-1)
|
71 |
+
# metric = evaluate.load("accuracy")
|
72 |
+
# return metric.compute(predictions=predictions, references=labels)
|
73 |
+
|
74 |
+
def compute_metrics(eval_preds):
|
75 |
+
preds, labels = eval_preds
|
76 |
+
|
77 |
+
# decode preds and labels
|
78 |
+
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
|
79 |
+
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
|
80 |
+
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
81 |
+
|
82 |
+
# rougeLSum expects newline after each sentence
|
83 |
+
decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
|
84 |
+
decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
|
85 |
+
|
86 |
+
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
|
87 |
+
|
88 |
+
return result
|
89 |
+
|
90 |
+
|
91 |
+
def training():
|
92 |
+
dataset_id = "tomasmcz/word2vec_analogy"
|
93 |
+
# dataset_id = "relbert/scientific_and_creative_analogy"
|
94 |
+
# dataset_sub = "Quadruples_Kmiecik_random_split"
|
95 |
+
print("GETTING DATASET")
|
96 |
+
dataset = load_dataset(dataset_id)
|
97 |
+
# dataset = dataset["train"]
|
98 |
+
# tokenized_datasets = dataset.map(tokenize_function, batched=True)
|
99 |
+
|
100 |
+
print(dataset)
|
101 |
+
print(f"- The {dataset_id} dataset has {dataset['train'].num_rows} examples.")
|
102 |
+
print(f"- Each example is a {type(dataset['train'][0])} with a {type(dataset['train'][0])} as value.")
|
103 |
+
print(f"- Examples look like this: {dataset['train'][0]}")
|
104 |
+
|
105 |
+
# for i in dataset["train"]:
|
106 |
+
# print(i["AB"], "to", i["CD"], "is", i["label"])
|
107 |
+
|
108 |
+
dataset = dataset["train"].train_test_split(test_size=0.3)
|
109 |
+
|
110 |
+
# We prefix our tasks with "answer the question"
|
111 |
+
prefix = "Please answer this question: "
|
112 |
+
|
113 |
+
|
114 |
+
def preprocess_function(examples):
|
115 |
+
"""Add prefix to the sentences, tokenize the text, and set the labels"""
|
116 |
+
# The "inputs" are the tokenized answer:
|
117 |
+
inputs = []
|
118 |
+
# print(examples)
|
119 |
+
# inputs = [prefix + doc for doc in examples["question"]]
|
120 |
+
for doc in examples['word_a']:
|
121 |
+
# print("THE DOC IS:", doc)
|
122 |
+
# print("THE DOC IS:", examples[i]['AB'], examples[i]['CD'], examples[i]['label'])
|
123 |
+
prompt = f"{prefix}{doc} is to "
|
124 |
+
inputs.append(prompt)
|
125 |
+
# inputs = [prefix + doc for doc in examples["question"]]
|
126 |
+
for indx, doc in enumerate(examples["word_b"]):
|
127 |
+
prompt = f"{doc} as "
|
128 |
+
inputs[indx] += prompt
|
129 |
+
|
130 |
+
for indx, doc in enumerate(examples["word_c"]):
|
131 |
+
prompt = f"{doc} is to ___."
|
132 |
+
inputs[indx] += prompt
|
133 |
+
model_inputs = tokenizer(inputs, max_length=128, truncation=True)
|
134 |
+
|
135 |
+
# print(examples["label"], type(examples["label"]))
|
136 |
+
|
137 |
+
# The "labels" are the tokenized outputs:
|
138 |
+
labels = tokenizer(text_target=examples["word_d"],
|
139 |
+
max_length=512,
|
140 |
+
truncation=True)
|
141 |
+
|
142 |
+
model_inputs["labels"] = labels["input_ids"]
|
143 |
+
return model_inputs
|
144 |
+
|
145 |
+
|
146 |
+
|
147 |
+
# Map the preprocessing function across our dataset
|
148 |
+
tokenized_dataset = dataset.map(preprocess_function, batched=True)
|
149 |
+
|
150 |
+
print("END DATALOADER")
|
151 |
+
|
152 |
+
# print(train_examples)
|
153 |
+
|
154 |
+
embeddings = finetune(tokenized_dataset)
|
155 |
+
|
156 |
+
return 0
|
157 |
+
|
158 |
+
|
159 |
+
def finetune(dataset):
|
160 |
+
# model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
161 |
+
# model_id = "sentence-transformers/all-MiniLM-L6-v2"
|
162 |
+
model_id = "google/flan-t5-base"
|
163 |
+
# model_id = "distilbert-base-uncased"
|
164 |
+
# tokenizer = DistilBertTokenizerFast.from_pretrained(model_id)
|
165 |
+
tokenizer = T5Tokenizer.from_pretrained(model_id)
|
166 |
+
model = T5ForConditionalGeneration.from_pretrained(model_id)
|
167 |
+
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
|
168 |
+
device = torch.device('cuda:0')
|
169 |
+
model = model.to(device)
|
170 |
+
|
171 |
+
# training_args = TrainingArguments(output_dir="test_trainer")
|
172 |
+
|
173 |
+
# USE THIS LINK
|
174 |
+
# https://huggingface.co/blog/how-to-train-sentence-transformers
|
175 |
+
|
176 |
+
# train_loss = losses.MegaBatchMarginLoss(model=model)
|
177 |
+
# ds_train, ds_valid = dataset.train_test_split(test_size=0.2, seed=42)
|
178 |
+
|
179 |
+
print("BEGIN FIT")
|
180 |
+
|
181 |
+
trainer = Seq2SeqTrainer(
|
182 |
+
model=model,
|
183 |
+
args=training_args,
|
184 |
+
train_dataset=dataset["train"],
|
185 |
+
eval_dataset=dataset["test"],
|
186 |
+
# evaluation_strategy="no"
|
187 |
+
tokenizer=tokenizer,
|
188 |
+
data_collator=data_collator,
|
189 |
+
compute_metrics=compute_metrics
|
190 |
+
)
|
191 |
+
|
192 |
+
# model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=10)
|
193 |
+
|
194 |
+
trainer.train()
|
195 |
+
|
196 |
+
# model.save("flan-analogies")
|
197 |
+
|
198 |
+
# model.save_to_hub("smhavens/bert-base-analogies")
|
199 |
+
# accuracy = compute_metrics(eval, metric)
|
200 |
+
return 0
|
201 |
+
|
202 |
+
def greet(name):
|
203 |
+
return "Hello " + name + "!!"
|
204 |
+
|
205 |
+
def check_answer(guess:str):
|
206 |
+
global guesses
|
207 |
+
global answer
|
208 |
+
guesses.append(guess)
|
209 |
+
output = ""
|
210 |
+
for guess in guesses:
|
211 |
+
output += ("- " + guess + "\n")
|
212 |
+
output = output[:-1]
|
213 |
+
|
214 |
+
if guess.lower() == answer.lower():
|
215 |
+
return "Correct!", output
|
216 |
+
else:
|
217 |
+
return "Try again!", output
|
218 |
+
|
219 |
+
def main():
|
220 |
+
print("BEGIN")
|
221 |
+
word1 = "Black"
|
222 |
+
word2 = "White"
|
223 |
+
word3 = "Sun"
|
224 |
+
global answer
|
225 |
+
answer = "Moon"
|
226 |
+
global guesses
|
227 |
+
|
228 |
+
training()
|
229 |
+
|
230 |
+
|
231 |
+
|
232 |
+
|
233 |
+
|
234 |
+
if __name__ == "__main__":
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
235 |
main()
|
results/checkpoint-16000/added_tokens.json
ADDED
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"<extra_id_0>": 32099,
|
3 |
+
"<extra_id_10>": 32089,
|
4 |
+
"<extra_id_11>": 32088,
|
5 |
+
"<extra_id_12>": 32087,
|
6 |
+
"<extra_id_13>": 32086,
|
7 |
+
"<extra_id_14>": 32085,
|
8 |
+
"<extra_id_15>": 32084,
|
9 |
+
"<extra_id_16>": 32083,
|
10 |
+
"<extra_id_17>": 32082,
|
11 |
+
"<extra_id_18>": 32081,
|
12 |
+
"<extra_id_19>": 32080,
|
13 |
+
"<extra_id_1>": 32098,
|
14 |
+
"<extra_id_20>": 32079,
|
15 |
+
"<extra_id_21>": 32078,
|
16 |
+
"<extra_id_22>": 32077,
|
17 |
+
"<extra_id_23>": 32076,
|
18 |
+
"<extra_id_24>": 32075,
|
19 |
+
"<extra_id_25>": 32074,
|
20 |
+
"<extra_id_26>": 32073,
|
21 |
+
"<extra_id_27>": 32072,
|
22 |
+
"<extra_id_28>": 32071,
|
23 |
+
"<extra_id_29>": 32070,
|
24 |
+
"<extra_id_2>": 32097,
|
25 |
+
"<extra_id_30>": 32069,
|
26 |
+
"<extra_id_31>": 32068,
|
27 |
+
"<extra_id_32>": 32067,
|
28 |
+
"<extra_id_33>": 32066,
|
29 |
+
"<extra_id_34>": 32065,
|
30 |
+
"<extra_id_35>": 32064,
|
31 |
+
"<extra_id_36>": 32063,
|
32 |
+
"<extra_id_37>": 32062,
|
33 |
+
"<extra_id_38>": 32061,
|
34 |
+
"<extra_id_39>": 32060,
|
35 |
+
"<extra_id_3>": 32096,
|
36 |
+
"<extra_id_40>": 32059,
|
37 |
+
"<extra_id_41>": 32058,
|
38 |
+
"<extra_id_42>": 32057,
|
39 |
+
"<extra_id_43>": 32056,
|
40 |
+
"<extra_id_44>": 32055,
|
41 |
+
"<extra_id_45>": 32054,
|
42 |
+
"<extra_id_46>": 32053,
|
43 |
+
"<extra_id_47>": 32052,
|
44 |
+
"<extra_id_48>": 32051,
|
45 |
+
"<extra_id_49>": 32050,
|
46 |
+
"<extra_id_4>": 32095,
|
47 |
+
"<extra_id_50>": 32049,
|
48 |
+
"<extra_id_51>": 32048,
|
49 |
+
"<extra_id_52>": 32047,
|
50 |
+
"<extra_id_53>": 32046,
|
51 |
+
"<extra_id_54>": 32045,
|
52 |
+
"<extra_id_55>": 32044,
|
53 |
+
"<extra_id_56>": 32043,
|
54 |
+
"<extra_id_57>": 32042,
|
55 |
+
"<extra_id_58>": 32041,
|
56 |
+
"<extra_id_59>": 32040,
|
57 |
+
"<extra_id_5>": 32094,
|
58 |
+
"<extra_id_60>": 32039,
|
59 |
+
"<extra_id_61>": 32038,
|
60 |
+
"<extra_id_62>": 32037,
|
61 |
+
"<extra_id_63>": 32036,
|
62 |
+
"<extra_id_64>": 32035,
|
63 |
+
"<extra_id_65>": 32034,
|
64 |
+
"<extra_id_66>": 32033,
|
65 |
+
"<extra_id_67>": 32032,
|
66 |
+
"<extra_id_68>": 32031,
|
67 |
+
"<extra_id_69>": 32030,
|
68 |
+
"<extra_id_6>": 32093,
|
69 |
+
"<extra_id_70>": 32029,
|
70 |
+
"<extra_id_71>": 32028,
|
71 |
+
"<extra_id_72>": 32027,
|
72 |
+
"<extra_id_73>": 32026,
|
73 |
+
"<extra_id_74>": 32025,
|
74 |
+
"<extra_id_75>": 32024,
|
75 |
+
"<extra_id_76>": 32023,
|
76 |
+
"<extra_id_77>": 32022,
|
77 |
+
"<extra_id_78>": 32021,
|
78 |
+
"<extra_id_79>": 32020,
|
79 |
+
"<extra_id_7>": 32092,
|
80 |
+
"<extra_id_80>": 32019,
|
81 |
+
"<extra_id_81>": 32018,
|
82 |
+
"<extra_id_82>": 32017,
|
83 |
+
"<extra_id_83>": 32016,
|
84 |
+
"<extra_id_84>": 32015,
|
85 |
+
"<extra_id_85>": 32014,
|
86 |
+
"<extra_id_86>": 32013,
|
87 |
+
"<extra_id_87>": 32012,
|
88 |
+
"<extra_id_88>": 32011,
|
89 |
+
"<extra_id_89>": 32010,
|
90 |
+
"<extra_id_8>": 32091,
|
91 |
+
"<extra_id_90>": 32009,
|
92 |
+
"<extra_id_91>": 32008,
|
93 |
+
"<extra_id_92>": 32007,
|
94 |
+
"<extra_id_93>": 32006,
|
95 |
+
"<extra_id_94>": 32005,
|
96 |
+
"<extra_id_95>": 32004,
|
97 |
+
"<extra_id_96>": 32003,
|
98 |
+
"<extra_id_97>": 32002,
|
99 |
+
"<extra_id_98>": 32001,
|
100 |
+
"<extra_id_99>": 32000,
|
101 |
+
"<extra_id_9>": 32090
|
102 |
+
}
|
results/checkpoint-16000/config.json
ADDED
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "google/flan-t5-base",
|
3 |
+
"architectures": [
|
4 |
+
"T5ForConditionalGeneration"
|
5 |
+
],
|
6 |
+
"classifier_dropout": 0.0,
|
7 |
+
"d_ff": 2048,
|
8 |
+
"d_kv": 64,
|
9 |
+
"d_model": 768,
|
10 |
+
"decoder_start_token_id": 0,
|
11 |
+
"dense_act_fn": "gelu_new",
|
12 |
+
"dropout_rate": 0.1,
|
13 |
+
"eos_token_id": 1,
|
14 |
+
"feed_forward_proj": "gated-gelu",
|
15 |
+
"initializer_factor": 1.0,
|
16 |
+
"is_encoder_decoder": true,
|
17 |
+
"is_gated_act": true,
|
18 |
+
"layer_norm_epsilon": 1e-06,
|
19 |
+
"model_type": "t5",
|
20 |
+
"n_positions": 512,
|
21 |
+
"num_decoder_layers": 12,
|
22 |
+
"num_heads": 12,
|
23 |
+
"num_layers": 12,
|
24 |
+
"output_past": true,
|
25 |
+
"pad_token_id": 0,
|
26 |
+
"relative_attention_max_distance": 128,
|
27 |
+
"relative_attention_num_buckets": 32,
|
28 |
+
"task_specific_params": {
|
29 |
+
"summarization": {
|
30 |
+
"early_stopping": true,
|
31 |
+
"length_penalty": 2.0,
|
32 |
+
"max_length": 200,
|
33 |
+
"min_length": 30,
|
34 |
+
"no_repeat_ngram_size": 3,
|
35 |
+
"num_beams": 4,
|
36 |
+
"prefix": "summarize: "
|
37 |
+
},
|
38 |
+
"translation_en_to_de": {
|
39 |
+
"early_stopping": true,
|
40 |
+
"max_length": 300,
|
41 |
+
"num_beams": 4,
|
42 |
+
"prefix": "translate English to German: "
|
43 |
+
},
|
44 |
+
"translation_en_to_fr": {
|
45 |
+
"early_stopping": true,
|
46 |
+
"max_length": 300,
|
47 |
+
"num_beams": 4,
|
48 |
+
"prefix": "translate English to French: "
|
49 |
+
},
|
50 |
+
"translation_en_to_ro": {
|
51 |
+
"early_stopping": true,
|
52 |
+
"max_length": 300,
|
53 |
+
"num_beams": 4,
|
54 |
+
"prefix": "translate English to Romanian: "
|
55 |
+
}
|
56 |
+
},
|
57 |
+
"tie_word_embeddings": false,
|
58 |
+
"torch_dtype": "float32",
|
59 |
+
"transformers_version": "4.35.2",
|
60 |
+
"use_cache": true,
|
61 |
+
"vocab_size": 32128
|
62 |
+
}
|
results/checkpoint-16000/generation_config.json
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"decoder_start_token_id": 0,
|
3 |
+
"eos_token_id": 1,
|
4 |
+
"pad_token_id": 0,
|
5 |
+
"transformers_version": "4.35.2"
|
6 |
+
}
|
results/checkpoint-16000/model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cd7f96db75733e18d6af8488ab51eea991be641c6c22b24fa5ab3b45101c3398
|
3 |
+
size 990345064
|
results/checkpoint-16000/optimizer.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:31aa07bcfc63b03b9dbfb77536457e4d0591b64d537e2f4834f5b81c6bd2ab21
|
3 |
+
size 1980860410
|
results/checkpoint-16000/rng_state.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cc296e1811c88d4548bfa74b8cf96485e58c41652ba8a0db69b6e3a9762f9be0
|
3 |
+
size 14244
|
results/checkpoint-16000/scheduler.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8c77d751bb87ca04afd8f823ee9102cffea6221900b1a056c2f31d9044f1a0ce
|
3 |
+
size 1064
|
results/checkpoint-16000/special_tokens_map.json
ADDED
@@ -0,0 +1,125 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"<extra_id_0>",
|
4 |
+
"<extra_id_1>",
|
5 |
+
"<extra_id_2>",
|
6 |
+
"<extra_id_3>",
|
7 |
+
"<extra_id_4>",
|
8 |
+
"<extra_id_5>",
|
9 |
+
"<extra_id_6>",
|
10 |
+
"<extra_id_7>",
|
11 |
+
"<extra_id_8>",
|
12 |
+
"<extra_id_9>",
|
13 |
+
"<extra_id_10>",
|
14 |
+
"<extra_id_11>",
|
15 |
+
"<extra_id_12>",
|
16 |
+
"<extra_id_13>",
|
17 |
+
"<extra_id_14>",
|
18 |
+
"<extra_id_15>",
|
19 |
+
"<extra_id_16>",
|
20 |
+
"<extra_id_17>",
|
21 |
+
"<extra_id_18>",
|
22 |
+
"<extra_id_19>",
|
23 |
+
"<extra_id_20>",
|
24 |
+
"<extra_id_21>",
|
25 |
+
"<extra_id_22>",
|
26 |
+
"<extra_id_23>",
|
27 |
+
"<extra_id_24>",
|
28 |
+
"<extra_id_25>",
|
29 |
+
"<extra_id_26>",
|
30 |
+
"<extra_id_27>",
|
31 |
+
"<extra_id_28>",
|
32 |
+
"<extra_id_29>",
|
33 |
+
"<extra_id_30>",
|
34 |
+
"<extra_id_31>",
|
35 |
+
"<extra_id_32>",
|
36 |
+
"<extra_id_33>",
|
37 |
+
"<extra_id_34>",
|
38 |
+
"<extra_id_35>",
|
39 |
+
"<extra_id_36>",
|
40 |
+
"<extra_id_37>",
|
41 |
+
"<extra_id_38>",
|
42 |
+
"<extra_id_39>",
|
43 |
+
"<extra_id_40>",
|
44 |
+
"<extra_id_41>",
|
45 |
+
"<extra_id_42>",
|
46 |
+
"<extra_id_43>",
|
47 |
+
"<extra_id_44>",
|
48 |
+
"<extra_id_45>",
|
49 |
+
"<extra_id_46>",
|
50 |
+
"<extra_id_47>",
|
51 |
+
"<extra_id_48>",
|
52 |
+
"<extra_id_49>",
|
53 |
+
"<extra_id_50>",
|
54 |
+
"<extra_id_51>",
|
55 |
+
"<extra_id_52>",
|
56 |
+
"<extra_id_53>",
|
57 |
+
"<extra_id_54>",
|
58 |
+
"<extra_id_55>",
|
59 |
+
"<extra_id_56>",
|
60 |
+
"<extra_id_57>",
|
61 |
+
"<extra_id_58>",
|
62 |
+
"<extra_id_59>",
|
63 |
+
"<extra_id_60>",
|
64 |
+
"<extra_id_61>",
|
65 |
+
"<extra_id_62>",
|
66 |
+
"<extra_id_63>",
|
67 |
+
"<extra_id_64>",
|
68 |
+
"<extra_id_65>",
|
69 |
+
"<extra_id_66>",
|
70 |
+
"<extra_id_67>",
|
71 |
+
"<extra_id_68>",
|
72 |
+
"<extra_id_69>",
|
73 |
+
"<extra_id_70>",
|
74 |
+
"<extra_id_71>",
|
75 |
+
"<extra_id_72>",
|
76 |
+
"<extra_id_73>",
|
77 |
+
"<extra_id_74>",
|
78 |
+
"<extra_id_75>",
|
79 |
+
"<extra_id_76>",
|
80 |
+
"<extra_id_77>",
|
81 |
+
"<extra_id_78>",
|
82 |
+
"<extra_id_79>",
|
83 |
+
"<extra_id_80>",
|
84 |
+
"<extra_id_81>",
|
85 |
+
"<extra_id_82>",
|
86 |
+
"<extra_id_83>",
|
87 |
+
"<extra_id_84>",
|
88 |
+
"<extra_id_85>",
|
89 |
+
"<extra_id_86>",
|
90 |
+
"<extra_id_87>",
|
91 |
+
"<extra_id_88>",
|
92 |
+
"<extra_id_89>",
|
93 |
+
"<extra_id_90>",
|
94 |
+
"<extra_id_91>",
|
95 |
+
"<extra_id_92>",
|
96 |
+
"<extra_id_93>",
|
97 |
+
"<extra_id_94>",
|
98 |
+
"<extra_id_95>",
|
99 |
+
"<extra_id_96>",
|
100 |
+
"<extra_id_97>",
|
101 |
+
"<extra_id_98>",
|
102 |
+
"<extra_id_99>"
|
103 |
+
],
|
104 |
+
"eos_token": {
|
105 |
+
"content": "</s>",
|
106 |
+
"lstrip": false,
|
107 |
+
"normalized": false,
|
108 |
+
"rstrip": false,
|
109 |
+
"single_word": false
|
110 |
+
},
|
111 |
+
"pad_token": {
|
112 |
+
"content": "<pad>",
|
113 |
+
"lstrip": false,
|
114 |
+
"normalized": false,
|
115 |
+
"rstrip": false,
|
116 |
+
"single_word": false
|
117 |
+
},
|
118 |
+
"unk_token": {
|
119 |
+
"content": "<unk>",
|
120 |
+
"lstrip": false,
|
121 |
+
"normalized": false,
|
122 |
+
"rstrip": false,
|
123 |
+
"single_word": false
|
124 |
+
}
|
125 |
+
}
|
results/checkpoint-16000/spiece.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
|
3 |
+
size 791656
|
results/checkpoint-16000/tokenizer_config.json
ADDED
@@ -0,0 +1,939 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|