def run_eval(ckpt_path='/content/drive/My Drive/Text_summarization/BERT_text_summarisation/cnn_checkpoints/ckpt-69'):
restore_chkpt(ckpt_path)
if config.use_tfds:
examples, metadata = tfds.load(
config.tfds_name,
with_info=True,
as_supervised=True,
data_dir='/content/drive/My Drive/Text_summarization/cnn_dataset',
builder_kwargs={"version": "2.0.0"}
)
test_examples = examples['test']
test_buffer_size = metadata.splits['test'].num_examples
test_dataset = map_batch_shuffle(
test_examples,
test_buffer_size,
split='test',
batch_size=h_parms.batch_size
)
log.info('Test TF_dataset created')
test_dataset = test_dataset.take(1)
else:
test_dataset = infer_data_from_df()
ref_sents=[]
hyp_sents=[]
for (doc_id, (input_ids, _, _, target_ids, _, _)) in tqdm(enumerate(test_dataset, 1)):
start_time = time.time()
draft, refined_summary, att = predict_using_beam_search(
input_ids,
beam_size=3,
refine_decoder_type='greedy'
)
for tar, ref_hyp in zip(target_ids, refined_summary):
sum_ref = tokenizer.convert_ids_to_tokens([i for i in tf.squeeze(tar) if i not in [0, 101, 102]])
sum_hyp = tokenizer.convert_ids_to_tokens([i for i in tf.squeeze(ref_hyp) if i not in [0, 101, 102]])
sum_ref = convert_wordpiece_to_words(sum_ref)
sum_hyp = convert_wordpiece_to_words(sum_hyp)
ref_sents.append(sum_ref)
hyp_sents.append(sum_hyp)
try:
rouges = rouge_all.get_scores(ref_sents , hyp_sents)
avg_rouge_f1 = np.mean([np.mean([rouge_scores['rouge-1']["f"],
rouge_scores['rouge-2']["f"],
rouge_scores['rouge-l']["f"]]) for rouge_scores in rouges])
_, _, bert_f1 = b_score(ref_sents, hyp_sents, lang='en', model_type=config.pretrained_bert_model)
avg_bert_f1 = np.mean(bert_f1.numpy())
except:
avg_rouge_f1 = 0
avg_bert_f1 = 0
print(infer_template.format('beam_search', 'greedy', avg_rouge_f1, avg_bert_f1, 3))
print(f'time to process document {doc_id} : {time.time()-start_time}')
print(f'Calculating scores for {len(ref_sents)} golden summaries and {len(hyp_sents)} predicted summaries')
])))
print('Predicted summary: {}'.format(
tokenizer.decode([
j for j in tf.squeeze(translated_output_temp[0][:, 0, :])
if j < tokenizer.vocab_size
])))
ref_sents = [ref for ref, _ in total_summary]
hyp_sents = [hyp for _, hyp in total_summary]
rouges = rouge_all.get_scores(ref_sents, hyp_sents)
avg_rouge_f1 = np.mean([
np.mean([
rouge_scores['rouge-1']["f"], rouge_scores['rouge-2']["f"],
rouge_scores['rouge-l']["f"]
]) for rouge_scores in rouges
])
_, _, bert_f1 = b_score(ref_sents,
hyp_sents,
lang='en',
model_type='bert-base-uncased')
print(
infer_template.format(beam_size, avg_rouge_f1,
np.mean(bert_f1.numpy())))
print(f'time to process document {doc_id} : {time.time()-start_time}')
if __name__ == '__main__':
#Restore the model's checkpoints
restore_chkpt(file_path.infer_ckpt_path)
infer_dataset = infer_data_from_df()
run_inference(infer_dataset)