def test_run_summarization(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--max_steps=50
--warmup_steps=8
--do_train
--do_eval
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(sys, "argv", testargs):
run_summarization.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_rouge1"], 10)
self.assertGreaterEqual(result["eval_rouge2"], 2)
self.assertGreaterEqual(result["eval_rougeL"], 7)
self.assertGreaterEqual(result["eval_rougeLsum"], 7)
def main(text):
_text_to_binary(text, '../news-summarizer-ai/pointer-generator/data/val')
run_summarization.main('work')
with open(DECODE_PATH, 'r', encoding="utf-8") as f:
abstract = f.read()
sent_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
sentences = sent_tokenizer.tokenize(abstract) # this text should be summary
sentences = [sent.capitalize() for sent in sentences]
result = ' '.join(sentences)
print("DIMAAAAA")
print(json.dumps(result))
def test():
run_summarization.main('Stories', "Summaries", 5, 0.95, 100, 200)
def main():
# start flask app
run_summarization.main()
app.run(host='0.0.0.0')