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| author | zhang <zch921005@126.com> | 2022-07-03 12:49:17 +0800 |
|---|---|---|
| committer | zhang <zch921005@126.com> | 2022-07-03 12:49:17 +0800 |
| commit | 8131406d83acad62de49ad51f219b3d2dba562d8 (patch) | |
| tree | 223b630dcd131bbd1d1e061a3c45dd719f2238eb | |
| parent | 8c1d5025e8a7c341ea651821222229ca75cd8208 (diff) | |
torch.no_grad vs. requires_grad
| -rw-r--r-- | fine_tune/bert/tutorials/03_bert_input_embedding.py | 20 | ||||
| -rw-r--r-- | fine_tune/bert/tutorials/samples/BERT-embeddings-2.png | bin | 0 -> 23064 bytes | |||
| -rw-r--r-- | fine_tune/bert_parameters.py | 2 | ||||
| -rw-r--r-- | fine_tune/input_output.py | 13 | ||||
| -rw-r--r-- | myweb/demo/app.py | 11 | ||||
| -rw-r--r-- | myweb/demo/templates/index.html | 4 |
6 files changed, 50 insertions, 0 deletions
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 Binary files differnew file mode 100644 index 0000000..9b7072e --- /dev/null +++ b/fine_tune/bert/tutorials/samples/BERT-embeddings-2.png 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 @@ + +<html> +hello world! +</html>
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