初始化项目,由ModelHub XC社区提供模型
Model: flax-community/papuGaPT2 Source: Original Platform
This commit is contained in:
3
allegro_reviews/config.json
Normal file
3
allegro_reviews/config.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1ace5aef92f7880ccb5fd0e7c5f65556d6914dbd134fa1672b46a0533225c036
|
||||
size 811
|
||||
6
allegro_reviews/create_config_allegro.py
Executable file
6
allegro_reviews/create_config_allegro.py
Executable file
@@ -0,0 +1,6 @@
|
||||
from transformers import GPT2Config
|
||||
|
||||
model_dir = "." # ${MODEL_DIR}
|
||||
|
||||
config = GPT2Config.from_pretrained("gpt2", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0)
|
||||
config.save_pretrained(model_dir)
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:25a5b7d6e069647cf953e1684211cf4b87049ae4e05610e37b1047966bd36fcc
|
||||
size 40
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ee76cbdc38f6bec33ee28c5225264d95b8d46c0a2941ce59fbe8893f798a3de8
|
||||
size 40
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d20520f97baa97ebd08bbf9f66afb294613261a1661dbd9bf18ca39b4258e03d
|
||||
size 40
|
||||
3
allegro_reviews/tokenizer.json
Normal file
3
allegro_reviews/tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c1735fd67aa6471a45e6baf09a106fdd7545046f3a805b0820a5d5fcb34ccf76
|
||||
size 1515050
|
||||
26
allegro_reviews/train_tokenizer_allegro.py
Executable file
26
allegro_reviews/train_tokenizer_allegro.py
Executable file
@@ -0,0 +1,26 @@
|
||||
from datasets import load_dataset
|
||||
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
|
||||
|
||||
model_dir = "." # ${MODEL_DIR}
|
||||
|
||||
# load dataset
|
||||
dataset = load_dataset("allegro_reviews", split="train")
|
||||
|
||||
# Instantiate tokenizer
|
||||
tokenizer = ByteLevelBPETokenizer()
|
||||
|
||||
def batch_iterator(batch_size=1000):
|
||||
for i in range(0, len(dataset), batch_size):
|
||||
yield dataset[i: i + batch_size]["text"]
|
||||
|
||||
# Customized training
|
||||
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
|
||||
"<s>",
|
||||
"<pad>",
|
||||
"</s>",
|
||||
"<unk>",
|
||||
"<mask>",
|
||||
])
|
||||
|
||||
# Save files to disk
|
||||
tokenizer.save(f"{model_dir}/tokenizer.json")
|
||||
Reference in New Issue
Block a user