scibert | A BERT model for scientific text | Natural Language Processing library
kandi X-RAY | scibert Summary
kandi X-RAY | scibert Summary
SciBERT models include all necessary files to be plugged in your own model and are in same format as BERT. If you are using Tensorflow, refer to Google's BERT repo and if you use PyTorch, refer to Hugging Face's repo where detailed instructions on using BERT models are provided.
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Top functions reviewed by kandi - BETA
- Compute the full table
- Calculate the difference between two scores
- Compute the conservative argument matrix
- Processes a part of a paper
- Convert a paper record to spacy text
- Process a paper record
- Get a list of sentences from spacy text
- Get spacy nlp
- Convert a JSON file to JSON
- Converts a list of dictionaries into text format
- Processes a paper file
- Processes a piece of papers
scibert Key Features
scibert Examples and Code Snippets
Lines of Code : 79License : Permissive (MIT)
Copyfrom transformers import pipeline
text = "Mouse thymus was used as a source of glucocorticoid receptor from normal CS lymphocytes."
nlp_ner = pipeline("ner",
model='fran-martinez/scibert_scivocab_cased_ner_jnlpba',
export NEW_TSV_DIR=sample/tsv
export FINGERPRINT_DIR=sample/radius1
export RADIUS=1
python3 fingerprint/preprocessor.py $NEW_TSV_DIR none $RADIUS $FINGERPRINT_DIR
cd main
python run_ddie.py \
--task_name MRPC \
--model_type bert \
--data ├── ner
│ ├── JNLPBA
│ ├── NCBI-disease
│ ├── bc5cdr
│ └── sciie
├── parsing
│ └── genia
├── pico
│ └── ebmnlp
└── text_classification
├── chemprot
├── citation_intent
Community Discussions
Trending Discussions on scibert
QUESTION
I'm trying to use BERT models to do text classification. As the text is about scientific texts, I intend to use the SicBERT pre-trained model: https://github.com/allenai/scibert
I have faced several limitations which I want to know if there is any solutions for them:
When I want to do tokenization and batching, it only allows me to use
max_lengthof <=512. Is there any way to use more tokens. Doen't this limitation of 512 mean that I am actually not using all the text information during training? Any solution to use all the text?I have tried to use this pretrained library with other models such as DeBERTa or RoBERTa. But it doesn't let me. I has only worked with BERT. Is there anyway I can do that?
I know this is a general question, but any suggestion that I can improve my fine tuning (from data to hyper parameter, etc)? Currently, I'm getting ~75% accuracy. Thanks
Codes:
...ANSWER
Answered 2021-Oct-03 at 14:21When I want to do tokenization and batching, it only allows me to use max_length of <=512. Is there any way to use more tokens. Doen't this limitation of 512 mean that I am actually not using all the text information during training? Any solution to use all the text?
Yes, you are not using the complete text. And this is one of the limitations of BERT and T5 models, which limit to using 512 and 1024 tokens resp. to the best of my knowledge.
I can suggest you to use Longformer or Bigbird or Reformer models, which can handle sequence lengths up to 16k, 4096, 64k tokens respectively. These are really good for processing longer texts like scientific documents.
I have tried to use this pretrained library with other models such as DeBERTa or RoBERTa. But it doesn't let me. I has only worked with BERT. Is there anyway I can do that?
SciBERT is actually a pre-trained BERT model.
See this issue for more details where they mention the feasibility of converting BERT to ROBERTa:
Since you're working with a BERT model that was pre-trained, you unfortunately won't be able to change the tokenizer now from a WordPiece (BERT) to a Byte-level BPE (RoBERTa).
I know this is a general question, but any suggestion that I can improve my fine tuning (from data to hyper parameter, etc)? Currently, I'm getting ~79% accuracy.
I would first try to tune the most important hyperparameter learning_rate. I would then explore different values for hyperparameters of AdamW optimizer and num_warmup_steps hyperparamter of the scheduler.
QUESTION
I am trying to use the pretrained SciBERT model (https://huggingface.co/allenai/scibert_scivocab_uncased) from Huggingface to predict masked words in scientific/biomedical text. This produces errors, and not sure how to move forward from this point.
Here is the code so far -
...ANSWER
Answered 2021-Jun-07 at 14:28As the error message tells you, you need to use AutoModelForMaskedLM:
QUESTION
I’m trying to train BERT model from scratch using my own dataset using HuggingFace library. I would like to train the model in a way that it has the exact architecture of the original BERT model.
In the original paper, it stated that: “BERT is trained on two tasks: predicting randomly masked tokens (MLM) and predicting whether two sentences follow each other (NSP). SCIBERT follows the same architecture as BERT but is instead pretrained on scientific text.”
I’m trying to understand how to train the model on two tasks as above. At the moment, I initialised the model as below:
...ANSWER
Answered 2021-Feb-10 at 14:04I would suggest doing the following:
First pre-train BERT on the MLM objective. HuggingFace provides a script especially for training BERT on the MLM objective on your own data. You can find it here. As you can see in the
run_mlm.pyscript, they useAutoModelForMaskedLM, and you can specify any architecture you want.Second, if want to train on the next sentence prediction task, you can define a
BertForPretrainingmodel (which has both the MLM and NSP heads on top), then load in the weights from the model you trained in step 1, and then further pre-train it on a next sentence prediction task.
UPDATE: apparently the next sentence prediction task did help improve performance of BERT on some GLUE tasks. See this talk by the author of BERT.
QUESTION
Trying to use text classifier model shared by https://github.com/allenai/scibert/blob/master/scibert/models/text_classifier.py
Everything used to work and suddenly I keep getting this error: Cannot register text_classifier as Model; name already in use for TextClassifier
What might be the reason? any suggestion?
...ANSWER
Answered 2021-Feb-17 at 13:55The name is already taken. Something that’s already a part of AllenNLP uses that name already, so you need to pick a different one.
For the curious, AllenNLP creates a registry of models, so that you can select a model at the command line. (That’s what the decorator is doing.) This requires the names to be unique.
The name text_classifier was used by AllenNLP only after the external package you’re using used it. It worked in May 2019, when that file was last updated. But 17 months ago, AllenNLP started using it. So it’s not your fault; it’s a mismatch between those two packages (at least, in their current versions).
QUESTION
I am using the Scibert pretrained model to get embeddings for various texts. The code is as follows:
...ANSWER
Answered 2020-Nov-27 at 13:48truncation is not a parameter of the class constructor (class reference), but a parameter of the __call__ method. Therefore you should use:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install scibert
You can use scibert like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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