BERT_multimodal_transformer | Open source code for ACL 2020 Paper | Natural Language Processing library
kandi X-RAY | BERT_multimodal_transformer Summary
kandi X-RAY | BERT_multimodal_transformer Summary
Open source code for ACL 2020 Paper: Integrating Multimodal Information in Large Pretrained Transformers.
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Trending Discussions on Natural Language Processing
QUESTION
For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.
As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.
...ANSWER
Answered 2022-Apr-14 at 13:32Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.
QUESTION
I'm trying to figure out why Apple's Natural Language API returns unexpected results.
What am I doing wrong? Is it a grammar issue?
I have the following four strings, and I want to extract each word's "stem form."
...ANSWER
Answered 2022-Apr-01 at 20:30As for why the tagger doesn't find "accredit" from "accreditation", this is because the scheme .lemma finds the lemma of words, not actually the stems. See the difference between stem and lemma on Wikipedia.
The stem is the part of the word that never changes even when morphologically inflected; a lemma is the base form of the word. For example, from "produced", the lemma is "produce", but the stem is "produc-". This is because there are words such as production and producing In linguistic analysis, the stem is defined more generally as the analyzed base form from which all inflected forms can be formed.
The documentation uses the word "stem", but I do think that the lemma is what is intended here, and getting "accreditation" is the expected behaviour. See the Usage section of the Wikipedia article for "Word stem" for more info. The lemma is the dictionary form of a word, and "accreditation" has a dictionary entry, whereas something like "accredited" doesn't. Whatever you call these things, the point is that there are two distinct concepts, and the tagger gets you one of them, but you are expecting the other one.
As for why the order of the words matters, this is because the tagger tries to analyse your words as "natural language", rather than each one individually. Naturally, word order matters. If you use .lexicalClass, you'll see that it thinks the third word in text2 is an adjective, which explains why it doesn't think its dictionary form is "accredit", because adjectives don't conjugate like that. Note that accredited is an adjective in the dictionary. So "is it a grammar issue?" Exactly.
QUESTION
I am trying to clean up text using a pre-processing function. I want to remove all non-alpha characters such as punctuation and digits, but I would like to retain compound words that use a dash without splitting them (e.g. pre-tender, pre-construction).
...ANSWER
Answered 2022-Mar-29 at 09:14To remove all non-alpha characters but - between letters, you can use
QUESTION
Looping over a list of bigrams to search for, I need to create a boolean field for each bigram according to whether or not it is present in a tokenized pandas series. And I'd appreciate an upvote if you think this is a good question!
List of bigrams:
...ANSWER
Answered 2022-Feb-16 at 20:28You could use a regex and extractall:
QUESTION
Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.
Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.
ANSWER
Answered 2022-Feb-03 at 09:29I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic: https://github.com/deepset-ai/haystack/issues/2081
QUESTION
I have a dataset of tens of thousands of dialogues / conversations between a customer and customer support. These dialogues, which could be forum posts, or long-winded email conversations, have been hand-annotated to highlight the sentence containing the customers problem. For example:
Dear agent, I am writing to you because I have a very annoying problem with my washing machine. I bought it three weeks ago and was very happy with it. However, this morning the door does not lock properly. Please help
Dear customer.... etc
The highlighted sentence would be:
However, this morning the door does not lock properly.
- What approaches can I take to model this, so that in future I can automatically extract the customers problem? The domain of the datasets are broad, but within the hardware space, so it could be appliances, gadgets, machinery etc.
- What is this type of problem called? I thought this might be called "intent recognition", but most guides seem to refer to multiclass classification. The sentence either is or isn't the customers problem. I considered analysing each sentence and performing binary classification, but I'd like to explore options that take into account the context of the rest of the conversation if possible.
- What resources are available to research how to implement this in Python (using tensorflow or pytorch)
I found a model on HuggingFace which has been pre-trained with customer dialogues, and have read the research paper, so I was considering fine-tuning this as a starting point, but I only have experience with text (multiclass/multilabel) classification when it comes to transformers.
...ANSWER
Answered 2022-Feb-07 at 10:21This type of problem where you want to extract the customer problem from the original text is called Extractive Summarization and this type of task is solved by Sequence2Sequence models.
The main reason for this type of model being called Sequence2Sequence is because the input and the output of this model would both be text.
I recommend you to use a transformers model called Pegasus which has been pre-trained to predict a masked text, but its main application is to be fine-tuned for text summarization (extractive or abstractive).
This Pegasus model is listed on Transformers library, which provides you with a simple but powerful way of fine-tuning transformers with custom datasets. I think this notebook will be extremely useful as guidance and for understanding how to fine-tune this Pegasus model.
QUESTION
My current data-frame is:
...ANSWER
Answered 2022-Jan-06 at 12:13try
QUESTION
I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?
From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.
For example in this SO question they calculated it using the function
...ANSWER
Answered 2021-Dec-25 at 21:51There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.
As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:
QUESTION
I am working on some sentence formation like this:
...ANSWER
Answered 2021-Dec-12 at 17:53You can first replace the dictionary keys in sentence to {} so that you can easily format a string in loop. Then you can use itertools.product to create the Cartesian product of dictionary.values(), so you can simply loop over it to create your desired sentences.
QUESTION
We can create a model from AutoModel(TFAutoModel) function:
...ANSWER
Answered 2021-Dec-05 at 09:07The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model
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Install BERT_multimodal_transformer
Configure global_configs.py global_configs.py defines global constants for runnning experiments. Dimensions of data modality (text, acoustic, visual), cpu/gpu settings, and MAG's injection position. Default configuration is set to MOSI. For running experiments on MOSEI or on custom dataset, make sure that ACOUSTIC_DIM and VISUAL_DIM are set approperiately. os.environ["CUDA_VISIBLE_DEVICES"] = "0" os.environ["WANDB_PROGRAM"] = "multimodal_driver.py" DEVICE = torch.device("cuda:0") # MOSI SETTING ACOUSTIC_DIM = 74 VISUAL_DIM = 47 TEXT_DIM = 768 # MOSEI SETTING # ACOUSTIC_DIM = 74 # VISUAL_DIM = 35 # TEXT_DIM = 768 # CUSTOM DATASET # ACOUSTIC_DIM = ?? # VISUAL_DIM = ?? # TEXT_DIM = ?? XLNET_INJECTION_INDEX = 1
Download datasets Inside ./datasets folder, run ./download_datasets.sh to download MOSI and MOSEI datasets
Training MAG-BERT / MAG-XLNet on MOSI First, install python dependancies using pip install -r requirements.txt Training scripts: MAG-BERT python multimodal_driver.py --model bert-base-uncased MAG-XLNet python multimodal_driver.py --model xlnet-base-cased By default, multimodal_driver.py will attempt to create a Weights and Biases (W&B) project to log your runs and results. If you wish to disable W&B logging, set environment variable to WANDB_MODE=dryrun.
Model usage We would like to thank huggingface for providing and open-sourcing BERT / XLNet code for developing our models. Note that bert.py / xlnet.py are based on huggingface's implmentation. MAG from modeling import MAG hidden_size, beta_shift, dropout_prob = 768, 1e-3, 0.5 multimodal_gate = MAG(hidden_size, beta_shift, dropout_prob) fused_embedding = multimodal_gate(text_embedding, visual_embedding, acoustic_embedding) MAG-BERT from bert import MAG_BertForSequenceClassification class MultimodalConfig(object): def __init__(self, beta_shift, dropout_prob): self.beta_shift = beta_shift self.dropout_prob = dropout_prob multimodal_config = MultimodalConfig(beta_shift=1e-3, dropout_prob=0.5) model = MAG_BertForSequenceClassification.from_pretrained( 'bert-base-uncased', multimodal_config=multimodal_config, num_labels=1, ) outputs = model(input_ids, visual, acoustic, attention_mask, position_ids) logits = outputs[0] MAG-XLNet from xlnet import MAG_XLNetForSequenceClassification class MultimodalConfig(object): def __init__(self, beta_shift, dropout_prob): self.beta_shift = beta_shift self.dropout_prob = dropout_prob multimodal_config = MultimodalConfig(beta_shift=1e-3, dropout_prob=0.5) model = MAG_XLNet_ForSequenceClassification.from_pretrained( 'xlnet-base-cased', multimodal_config=multimodal_config, num_labels=1, ) outputs = model(input_ids, visual, acoustic, attention_mask, position_ids) logits = outputs[0] For MAG-BERT / MAG-XLNet usage, visual, acoustic are torch.FloatTensor of shape (batch_size, sequence_length, modality_dim). input_ids, attention_mask, position_ids are torch.LongTensor of shape (batch_size, sequence_length). For more details on how these tensors should be formatted / generated, please refer to multimodal_driver.py's convert_to_features method and huggingface's documentation
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