deepopinion / domain-adapted-atsc Goto Github PK

l/cached_train_restaurants_10mio_ep3_128_semeval2014-atsc
Traceback (most recent call last):
File "run_glue.py", line 475, in
main()
File "run_glue.py", line 428, in main
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
File "run_glue.py", line 267, in load_and_cache_examples
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
File "run_glue.py", line 267, in
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
AttributeError: 'list' object has no attribute 'input_ids'

Can you share some fine-tuned pre-training models? It will be helpful for reproducibility.

thanks for you code! one more question:

1. how long does post-training take with regard to domain post-training data and MRC data? how many GPUs and have you using distributed? Could you provide more detail about process of trainning?

when I run python prepare_laptop_reviews.py

I am getting this:

Found 0 laptop items
Loading and Filtering Reviews
Found 0 laptop reviews
Tokenizing laptop Reviews...
Segmented 0 laptop sentences
0it [00:00, ?it/s]
Removed 0 reviews due to overlap with SemEval Laptops Dataset
Done writing to data/transformed/laptop_corpus_0.txt

And this gives me laptop_corpus_0.txt file, which is empty file

I downloaded Amazon electronics reviews (meta_Electronics.json.gz, reviews_Electronics.json.gz) and putted them into data/raw folder

I have SemEval 2014 ABSA train and test data in data/raw/semeval214/ folder

Am I missing something? Could you please help me to figure it out?

PS Preparation of restaurant reviews worked just fine and I got transformed restaurant_corpus.

Hello.

Thanks for your work.

I tried to reproduce the results from this article https://arxiv.org/pdf/1908.11860.pdf

1. I use scripts for down-stream task preparation - mix dataset without conflict label.

**# mixed without conflict label
python prepare_semeval_datasets.py
--files "data/raw/semeval2014/SemEval-2014 ABSA Train Data v2.0 & Annotation Guidelines/Restaurants_Train_v2.xml"
"data/raw/semeval2014/SemEval-2014 ABSA Train Data v2.0 & Annotation Guidelines/Laptop_Train_v2.xml"
--output_dir data/transformed/mixed_noconfl
--istrain --noconfl

python prepare_semeval_datasets.py
--files "data/raw/semeval2014/SemEval-2014 ABSA Test Data - Gold Annotations/ABSA_Gold_TestData/Restaurants_Test_Gold.xml"
"data/raw/semeval2014/SemEval-2014 ABSA Test Data - Gold Annotations/ABSA_Gold_TestData/Laptops_Test_Gold.xml"
--output_dir data/transformed/mixed_noconfl --noconfl**

2. I download model BERT-ADA Joint (Restaurant + Laptops)

3. I run script run_glue.py. I did'nt change the parameters.

But I have metrics:
acc = 0.8447098976109215
f1_macro = 0.7806977430089042

It’s possible that I missed something?

When I generate data for pretraining, it failed.

I am using the data file data/transformed/restaurant_corpus_1000000.txt. I found a line with <200d> and it generate an empty sentence.

231072 Normally I love chipotle, but the Portions are always disappointing and the ingredients never seem very fr esh.
231073 My sister hates this location's chicken but love chipotle chicken normally, not sure what they do differen tly.
231074 <200d>

What is the purpose of added_tokens.json file in model files? it only has {} and nothing else?

It is a great repository. Can I refer to this repository and the corresponding paper in my repository (actually I have referred them in LCF-ABSA, but forgot to ask for permission)

the original finetune_on_pregenerated.py from hugging face/pytorch-transformers repository actually has some bugs:
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
actually "warmup,t_total" shouldn't be used.

Besides, I first run "Pregenerating training data", then the "Training on pregenerated data" as the readme from "huggingface," said. but the final files didn't improve the performance

from the code in the original finetune_on_pregenerated.py：
" output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)

torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)

"
the final finetuning model files are different from your provides;

can you provide finetuning code? appreciated.

Could you please provide the script which was used for Down-stream task-specific (SemEval 2014 Task 4) finetuning?
In the readme, it is mentioned that its an adaptation of https://github.com/huggingface/pytorch-transformers/blob/v1.0.0/examples/run_glue.py .However details of the adaptation are not mentioned.

Request you to please release your finetuning and evaluation code.

Hello. Great work! Thanks for publishing the code. I am very new to this field and I am trying to reproduce this work with semeval 2016 task 5 for Russian language. Could you please tell me what modifications I should make in order to prepare SemEval2016 Task 5 Russian restaurant Dataset for experiments?

if I want to predict the aspect sentiments of a new review, where should I edit?

Hi,

Thank s for sharing this great job with us.

Can u plz provide us with the tensorflow version of the post-trained bert models in laptop and restaurant domains?

I tried to convert the pytorch models to tensorflow but i did not work

Thank u