From 8131406d83acad62de49ad51f219b3d2dba562d8 Mon Sep 17 00:00:00 2001 From: zhang Date: Sun, 3 Jul 2022 12:49:17 +0800 Subject: torch.no_grad vs. requires_grad --- fine_tune/bert/tutorials/03_bert_input_embedding.py | 20 ++++++++++++++++++++ .../bert/tutorials/samples/BERT-embeddings-2.png | Bin 0 -> 23064 bytes fine_tune/bert_parameters.py | 2 ++ fine_tune/input_output.py | 13 +++++++++++++ myweb/demo/app.py | 11 +++++++++++ myweb/demo/templates/index.html | 4 ++++ 6 files changed, 50 insertions(+) create mode 100644 fine_tune/bert/tutorials/03_bert_input_embedding.py create mode 100644 fine_tune/bert/tutorials/samples/BERT-embeddings-2.png create mode 100644 fine_tune/input_output.py create mode 100644 myweb/demo/app.py create mode 100644 myweb/demo/templates/index.html diff --git a/fine_tune/bert/tutorials/03_bert_input_embedding.py b/fine_tune/bert/tutorials/03_bert_input_embedding.py new file mode 100644 index 0000000..41bdd29 --- /dev/null +++ b/fine_tune/bert/tutorials/03_bert_input_embedding.py @@ -0,0 +1,20 @@ + +from transformers import BertTokenizer, BertModel +from transformers.models.bert import BertModel +import torch +from torch import nn + + +model_name = 'bert-base-uncased' + +tokenizer = BertTokenizer.from_pretrained(model_name) +model = BertModel.from_pretrained(model_name, output_hidden_states=True) + +test_sent = 'this is a test sentence' + +model_input = tokenizer(test_sent, return_tensors='pt') + + +model.eval() +with torch.no_grad(): + output = model(**model_input) diff --git a/fine_tune/bert/tutorials/samples/BERT-embeddings-2.png b/fine_tune/bert/tutorials/samples/BERT-embeddings-2.png new file mode 100644 index 0000000..9b7072e Binary files /dev/null and b/fine_tune/bert/tutorials/samples/BERT-embeddings-2.png differ diff --git a/fine_tune/bert_parameters.py b/fine_tune/bert_parameters.py index 98e2b15..bf6e8c1 100644 --- a/fine_tune/bert_parameters.py +++ b/fine_tune/bert_parameters.py @@ -9,6 +9,8 @@ model_name = 'bert-base-uncased' model = BertModel.from_pretrained(model_name) cls_model = BertForSequenceClassification.from_pretrained(model_name) + + total_params = 0 total_learnable_params = 0 total_embedding_params = 0 diff --git a/fine_tune/input_output.py b/fine_tune/input_output.py new file mode 100644 index 0000000..684ded5 --- /dev/null +++ b/fine_tune/input_output.py @@ -0,0 +1,13 @@ + +from transformers import BertModel, BertTokenizer + +model_name = 'bert-base-uncased' + +tokenizer = BertTokenizer.from_pretrained(model_name) +model = BertModel.from_pretrained(model_name) + +raw_sentences = ['Tom likes cats', 'Liz likes dogs'] + +inputs = tokenizer.encode_plus(raw_sentences[0], raw_sentences[1], return_tensors='pt') +# inputs = tokenizer('Hello, my dog is cute', return_tensors='pt') +model(**inputs) diff --git a/myweb/demo/app.py b/myweb/demo/app.py new file mode 100644 index 0000000..86a9b08 --- /dev/null +++ b/myweb/demo/app.py @@ -0,0 +1,11 @@ +from flask import Flask, render_template + +app = Flask(__name__) + +@app.route('/') +def index(): + return render_template('index.html') + +if __name__ == '__main__': + app.run() + diff --git a/myweb/demo/templates/index.html b/myweb/demo/templates/index.html new file mode 100644 index 0000000..c5c4431 --- /dev/null +++ b/myweb/demo/templates/index.html @@ -0,0 +1,4 @@ + + +hello world! + \ No newline at end of file -- cgit v1.2.3