I want to input conversation data as an input to the gpt2 model from huggingface transformers.
====Example====
A: Where did you meet David?
B: I met him at the central park.
A: Weren't he quite strange that day?
=> predicted B: Not at all, why?
===============
Like the upper example, I want to input some conversation data to the transformer and get a reply from the pretrained model(gpt2). Can anybody tell me how?
Related
I am using Gensim's Doc2Vec, and was wondering if there is a way to get the most similar document to another document that is outside the list of TaggedDocuments used to train the Doc2Vec model.
Right now I can infer a vector from a document not in the training set:
# 'model' here is a instance of Doc2Vec class that has been trained
# Inferring a vector
doc_not_in_training_set = "Foo Foo Foo Foo Foo Foo Fie"
v1 = model.infer_vector(word_tokenize(doc_not_in_training_set.lower()))
print("V1_infer", v1)
This prints out a vector representation of the 'doc_not_in_training_set' string. However, is there a way to use this vector to find the n most similar documents to the 'doc_not_in_training_set' string (in the TaggedDocuments training set for this word2vec model)?
Looking under the documentation, the closest I could find was the model.docvec.most_similar() method:
# Finding most similar to first
similar_doc = model.docvecs.most_similar('0')
This returns the document in the training set most similar to the document in the training set with tag '0'.
In the documentation of this method, it looks like there is not yet the functionality I am looking for:
TODO: Accept vectors of out-of-training-set docs, as if from inference.
Is there another method I can use to find documents similar to a document not in the training set?
The .most_similar() method will also take a raw vectors as the target position.
It helps to explicitly name the positive parameter, to prevent other logic of that method, which tries to intuit what other strings/etc supplied as arguments might mean, from misinterpreting a single raw vector.
So try:
similar_docs = model.docvecs.most_similar(positive=[v1])
You should get back a list of nearest-neighbors to the v1 vector that you'd previously inferred.
I have few intents in my training set(nlu_data.md file) with sufficient amount of training examples under each intent.
Following is an example,
##intent: SEARCH_HOTEL
- find good [hotel](place) for me in Mumbai
I have added multiple sentences like this.
At the time of testing, all sentences in training file are working fine. But if any input query is having spelling mistake e.g, hotol/hetel/hotele for hotel keyword then Rasa NLU is unable to extract it as an entity.
I want to resolve this issue.
I am allowed to change only training data, also restricted not to write any custom component for this.
To handle spelling mistakes like this in entities, you should add these examples to your training data. So something like this:
##intent: SEARCH_HOTEL
- find good [hotel](place) for me in Mumbai
- looking for a [hotol](place) in Chennai
- [hetel](place) in Berlin please
Once you've added enough examples, the model should be able to generalise from the sentence structure.
If you're not using it already, it also makes sense to use the character-level CountVectorFeaturizer. That should be in the default pipeline described on this page already
One thing I would highly suggest you to use is to use look-up tables with fuzzywuzzy matching. If you have limited number of entities (like country names) look-up tables are quite fast, and fuzzy matching catches typos when that entity exists in your look-up table (searching for typo variations of those entities). There's a whole blogpost about it here: on Rasa.
There's a working implementation of fuzzy wuzzy as a custom component:
class FuzzyExtractor(Component):
name = "FuzzyExtractor"
provides = ["entities"]
requires = ["tokens"]
defaults = {}
language_list ["en"]
threshold = 90
def __init__(self, component_config=None, *args):
super(FuzzyExtractor, self).__init__(component_config)
def train(self, training_data, cfg, **kwargs):
pass
def process(self, message, **kwargs):
entities = list(message.get('entities'))
# Get file path of lookup table in json format
cur_path = os.path.dirname(__file__)
if os.name == 'nt':
partial_lookup_file_path = '..\\data\\lookup_master.json'
else:
partial_lookup_file_path = '../data/lookup_master.json'
lookup_file_path = os.path.join(cur_path, partial_lookup_file_path)
with open(lookup_file_path, 'r') as file:
lookup_data = json.load(file)['data']
tokens = message.get('tokens')
for token in tokens:
# STOP_WORDS is just a dictionary of stop words from NLTK
if token.text not in STOP_WORDS:
fuzzy_results = process.extract(
token.text,
lookup_data,
processor=lambda a: a['value']
if isinstance(a, dict) else a,
limit=10)
for result, confidence in fuzzy_results:
if confidence >= self.threshold:
entities.append({
"start": token.offset,
"end": token.end,
"value": token.text,
"fuzzy_value": result["value"],
"confidence": confidence,
"entity": result["entity"]
})
file.close()
message.set("entities", entities, add_to_output=True)
But I didn't implement it, it was implemented and validated here: Rasa forum
Then you will just pass it to your NLU pipeline in config.yml file.
Its a strange request that they ask you not to change the code or do custom components.
The approach you would have to take would be to use entity synonyms. A slight edit on a previous answer:
##intent: SEARCH_HOTEL
- find good [hotel](place) for me in Mumbai
- looking for a [hotol](place:hotel) in Chennai
- [hetel](place:hotel) in Berlin please
This way even if the user enters a typo, the correct entity will be extracted. If you want this to be foolproof, I do not recommend hand-editing the intents. Use some kind of automated tool for generating the training data. E.g. Generate misspelled words (typos)
First of all, add samples for the most common typos for your entities as advised here
Beyond this, you need a spellchecker.
I am not sure whether there is a single library that can be used in the pipeline, but if not you need to create a custom component. Otherwise, dealing with only training data is not feasible. You can't create samples for each typo.
Using Fuzzywuzzy is one of the ways, generally, it is slow and it doesn't solve all the issues.
Universal Encoder is another solution.
There should be more options for spell correction, but you will need to write code in any way.
I'm trying to finetune the pretrained Transformer-XL model transfo-xl-wt103 for a language modeling task. Therfore, I use the model class TransfoXLLMHeadModel.
To iterate over my dataset I use the LMOrderedIterator from the file tokenization_transfo_xl.py which yields a tensor with the data and its target for each batch (and the sequence length).
Let's assume the following data with batch_size = 1 and bptt = 8:
data = tensor([[1,2,3,4,5,6,7,8]])
target = tensor([[2,3,4,5,6,7,8,9]])
mems # from the previous output
My question is: I currently pass this data into the model like this:
output = model(input_ids=data, labels=target, mems=mems)
Is this correct?
I am wondering because the documentation says for the labels parameter:
labels (:obj:torch.LongTensor of shape :obj:(batch_size, sequence_length), optional, defaults to :obj:None):
Labels for language modeling.
Note that the labels are shifted inside the model, i.e. you can set lm_labels = input_ids
So what is it about the parameter lm_labels? I only see labels defined in the forward method.
And when the labels "are shifted" inside the model, does this mean I have to pass data twice (additionally instead of targets) because its shifted inside? But how does the model then know the next token to predict?
I also read through this bug and the fix in this pull request but I don't quite understand how to treat the model now (before vs. after fix)
Thanks in advance for some help!
Edit: Link to issue on Github
That does sound like a typo from another model's convention. You do have to pass data twice, once to input_ids and once to labels (in your case, [1, ... , 8] for both). The model will then attempt to predict [2, ... , 8] from [1, ... , 7]). I am not sure adding something at the beginning of the target tensor would work as that would probably cause size mismatches later down the line.
Passing twice is the default way to do this in transformers; before the aforementioned PR, TransfoXL did not shift labels internally and you had to shift the labels yourself. The PR changed it to be consistent with the library and the documentation, where you have to pass the same data twice.
I have calculated distances between two sentences using wmdistance() funtion of gensim with pre-trained model
Now, I want to similarity between them and tried with n_similarity() funnction, but keyerror occured
keyerror : word not in vacabulary
This shows screenshoot of error example
Anyone have got idea on this, please?
When you get an error that a word is not in the vocabulary, it means the word is not in that model.
Any attempt to look it up will generate a KeyError, to let you know you are trying to get a word-vector that isn't there.
You should filter your lists-of-tokens, before passing them to n_similarity(), to only include valid words.
Of course, that means you can't get a meaningful result about the word 'selfie'. It's unknown nonsense to the model, as if you asked for the word 'asruhfglaiwurfliuawiufsdfsdfs'.
am new to rasa_nlu am bulting a bot where it takes values that are dynamic i want the dynamic value to be captured by an entity
examples:
with notes please { bring your college marksheet }
with notes please { come prepared
where the string inside braces are dynamic values i need to capture them and make use of them. suggest me a way.
Thank you in advance
The Rasa component CRFEntityExtractor is able to generalize to different entity values. Just make sure you add enough training data, so that the additional random field can pick up the pattern.
The following example learns up to pick person names (you have to add more than these three examples, but this should give an idea).
## intent: greet_with_name
- Hi, I'm [Donald](name)
- Hello, it's [Steve](name)
- Hi, Im [Sam](name)
- ...