kandi X-RAY | language-model Summary
kandi X-RAY | language-model Summary
language-model
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Top functions reviewed by kandi - BETA
- Initialize the model .
- Iterate through the files .
- Evaluate the softmax function .
- Saves the word index .
- Predict the model .
- Restore the model .
- Train the optimizer on a given batch .
- Compile the optimizer .
- Set the data directory .
- Set sequence length
language-model Key Features
language-model Examples and Code Snippets
def get_translation_model_and_tokenizer(src_lang, dst_lang):
"""
Given the source and destination languages, returns the appropriate model
See the language codes here: https://developers.google.com/admin-sdk/directory/v1/languages
For the 3-c
Community Discussions
Trending Discussions on language-model
QUESTION
I am trying to retrieve embeddings for words based on the pretrained ELMo model available on tensorflow hub. The code I am using is modified from here: https://www.geeksforgeeks.org/overview-of-word-embedding-using-embeddings-from-language-models-elmo/
The sentence that I am inputting is
bod =" is coming up in and every project is expected to do a video due on we look forward to discussing this with you at our meeting this this time they have laid out the selection criteria for the video award s go for the top spot this time "
and these are the keywords I want embeddings for:
words=["do", "a", "video"]
ANSWER
Answered 2021-May-27 at 04:47This is not really an AllenNLP issue since you are using a tensorflow-based implementation of ELMo.
That said, I think the problem is that ELMo embeds tokens, not characters. You are getting 48 embeddings because the string has 48 tokens.
QUESTION
I have a subprocess that constantly listens to the microphone, converts the audio to text and stores the result. The code for this is
...ANSWER
Answered 2021-Mar-20 at 06:40You can try something like this:
QUESTION
Based on HuggingFace script to train a transformers model from scratch. I run:
...ANSWER
Answered 2021-Feb-26 at 04:50You have to make sure the followings are correct:
- GPU is correctly installed on your environment
QUESTION
Below is the code to configure TrainingArguments consumed from the HuggingFace transformers library to finetune the GPT2 language model.
...ANSWER
Answered 2021-Feb-01 at 06:18Pytorch provides several methods to adjust the learning_rate: torch.optim.lr_scheduler. Check the docs for usage https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
QUESTION
Resuming the GPT2
finetuning, implemented from run_clm.py
Does GPT2 huggingface has a parameter to resume the training from the saved checkpoint, instead training again from the beginning? Suppose the python notebook crashes while training, the checkpoints will be saved, but when I train the model again still it starts the training from the beginning.
Source: here
finetuning code:
...ANSWER
Answered 2021-Jan-18 at 14:07To resume training from checkpoint you use the --model_name_or_path
parameter. So instead of giving the default gpt2
you direct this to your latest checkpoint folder.
So your command becomes:
QUESTION
I'm using some domain-specific language which have a lot of OOV words as well as some typos. I have noticed Spacy will just assign an all-zero vector for these OOV words, so I'm wondering what's the proper way to handle this. I appreciate clarification on all of these points if possible:
- What exactly does the pre-train command do? Honestly I cannot seem to parse correctly the explanation from the website:
Pre-train the “token to vector” (tok2vec) layer of pipeline components, using an approximate language-modeling objective. Specifically, we load pretrained vectors, and train a component like a CNN, BiLSTM, etc to predict vectors which match the pretrained ones
Isn't the tok2vec the part that generates the vectors? So shouldn't this command then change the produced vectors? What does it mean loading pretrained vectors and then train a component to predict these vectors? What's the purpose of doing this?
What does the --use-vectors flag do? What does the --init-tok2vec flag do? Is this included by mistake in the documentation?
It seems pretrain is not what I'm looking for, it doesn't change the vectors for a given word. What would be the easiest way to generate a new set of vectors which includes my OOV words but still contain the general knowledge of the lanaguage?
As far as I can see Spacy's pretrained models use fasttext vectors. Fasttext website mentions:
A nice feature is that you can also query for words that did not appear in your data! Indeed words are represented by the sum of its substrings. As long as the unknown word is made of known substrings, there is a representation of it!
But it seems Spacy does not use this feature. Is there a way to still make use of this for OOV words?
Thanks a lot
...ANSWER
Answered 2020-Aug-21 at 09:32I think there is some confusion about the different components - I'll try to clarify:
- The tokenizer does not produce vectors. It's just a component that segments texts into tokens. In spaCy, it's rule-based and not trainable, and doesn't have anything to do with vectors. It looks at whitespace and punctuation to determine which are the unique tokens in a sentence.
- An
nlp
model in spaCy can have predefined (static) word vectors that are accessible on theToken
level. Every token with the same Lexeme gets the same vector. Some tokens/lexemes may indeed be OOV, like misspellings. If you want to redefine/extend all vectors used in a model, you can use something likeinit-model
. - The
tok2vec
layer is a machine learning component that learns how to produce suitable (dynamic) vectors for tokens. It does this by looking at lexical attributes of the token, but may also include the static vectors of the token (cf item 2). This component is generally not used by itself, but is part of another component, such as an NER. It will be the first layer of the NER model, and it can be trained as part of training the NER, to produce vectors that are suitable for your NER task.
In spaCy v2, you can first train a tok2vec component with pretrain
, and then use this component for a subsequent train
command. Note that all settings need to be the same across both commands, for the layers to be compatible.
To answer your questions:
Isn't the tok2vec the part that generates the vectors?
If you mean the static vectors, then no. The tok2vec component produces new vectors (possibly with a different dimension) on top of the static vectors, but it won't change the static ones.
What does it mean loading pretrained vectors and then train a component to predict these vectors? What's the purpose of doing this?
The purpose is to get a tok2vec
component that is already pretrained from external vectors data. The external vectors data already embeds some "meaning" or "similarity" of the tokens, and this is -so to say- transferred into the tok2vec
component, which learns to produce the same similarities. The point is that this new tok2vec
component can then be used & further fine-tuned in the subsequent train
command (cf item 3)
Is there a way to still make use of this for OOV words?
It really depends on what your "use" is. As https://stackoverflow.com/a/57665799/7961860 mentions, you can set the vectors yourself, or you can implement a user hook which will decide on how to define token.vector
.
I hope this helps. I can't really recommend the best approach for you to follow, without understanding why you want the OOV vectors / what your use-case is. Happy to discuss further in the comments!
QUESTION
Can you please tell me what I am missing in the code below? I am trying to use some functions defined (at the bottom of the post) that can help me to remove stopwords, form bigrams and doing some lemmatisation. The language is Italian. I am using space for doing so.
...ANSWER
Answered 2020-Jun-12 at 12:33!python -m spacy download it
QUESTION
I want to train BERT on a target corpus. I am looking at this HuggingFace implementation. They are using .raw files for the training data. If I have .txt files of my training data, how can I use their implementation?
...ANSWER
Answered 2020-May-28 at 19:13The .raw
only indicates that they use the raw version of the WikiText, they are regular text files containing the raw text:
We're using the raw WikiText-2 (no tokens were replaced before the tokenization).
The description of the data files options also says that they are text files. From run_language_modeling.py - L86-L88:
QUESTION
As part of my thesis, I am trying to build a recurrent Neural Network Language Model.
From theory, I know that the input layer should be a one-hot vector layer with a number of neurons equal to the number of words of our Vocabulary, followed by an Embedding layer, which, in Keras, it apparently translates to a single Embedding layer in a Sequential model. I also know that the output layer should also be the size of our vocabulary so that each output value maps 1-1 to each vocabulary word.
However, in both the Keras documentation for the Embedding layer (https://keras.io/layers/embeddings/) and in this article (https://machinelearningmastery.com/how-to-develop-a-word-level-neural-language-model-in-keras/#comment-533252), the vocabulary size is arbitrarily augmented by one for both the input and the output layers! Jason gives an explenation that this is due to the implementation of the Embedding layer in Keras but that doesn't explain why we would also use +1 neuron in the output layer. I am at the point of wanting to order the possible next words based on their probabilities and I have one probability too many that I do not know to which word to map it too.
Does anyone know what is the correct way of acheiving the desired result? Did Jason just forget to subtrack one from the output layer and the Embedding layer just needs a +1 for implementation reasons (I mean it's stated in the official API)?
Any help on the subject would be appreciated (why is Keras API documentation so laconic?).
Edit:
This post Keras embedding layer masking. Why does input_dim need to be |vocabulary| + 2? made me think that Jason does in fact have it wrong and that the size of the Vocabulary should not be incremented by one when our word indices are: 0, 1, ..., n-1
.
However, when using Keras's Tokenizer our word indices are: 1, 2, ..., n
. In this case, the correct approach is to:
Set
mask_zero=True
, to treat 0 differently, as there is never a 0 (integer) index input in the Embedding layer and keep the vocabulary size the same as the number of vocabulary words (n
)?Set
mask_zero=True
but augment the vocabulary size by one?Not set
mask_zero=True
and keep the vocabulary size the same as the number of vocabulary words?
ANSWER
Answered 2020-May-04 at 17:46the reason why we add +1 leads to the possibility that we can encounter a chance to see an unseen word(out of our vocabulary) during testing or in production, it is common to consider a generic term for those UNKNOWN and that is why we add a OOV
word in front which resembles all out of vocabulary words.
Check this issue on github which explains it in detail:
https://github.com/keras-team/keras/issues/3110#issuecomment-345153450
QUESTION
In his article 'Language Model Fine-Tuning For Pre-Trained Transformers' Thilina Rajapakse (https://medium.com/skilai/language-model-fine-tuning-for-pre-trained-transformers-b7262774a7ee)
provides the following code snippet for fine-tuning a pre-trained model using the library simpletransformers
:
ANSWER
Answered 2020-Apr-28 at 18:55Yes, the input to the train_model()
and eval_model()
methods need to be a single file.
Dynamically loading from multiple files will likely be supported in the future
Question 2Yes, you can use bert-base-multilingual-cased
model.
You will find a much more detailed, updated guide on language model training here.
Source - I am the creator of the library
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Install language-model
You can use language-model 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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