knowledgator / t5-for-ie

T5-for-information-extraction

This is an encoder-decoder model that was trained on various information extraction tasks, including text classification, named entity recognition, relation extraction and entity linking.

How to use:

First of all, initialize the model:

from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

device = torch.device("cuda") if torch.cuda.is_available() else torch.device('cpu')

tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")

model = T5ForConditionalGeneration.from_pretrained("knowledgator/t5-for-ie").to(device)

You need to set a prompt and put it with text to the model, below are examples of how to use it for different tasks:

named entity recognition

input_text = "Extract entity types from the text: <e1>Kyiv</e1> is the capital of <e2>Ukraine</e2>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

text classification

input_text = "Classify the following text into the most relevant categories: Kyiv is the capital of Ukraine"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

relation extraction

input_text = "Extract relations between entities in the text: <e1>Kyiv</e1> is the capital of <e2>Ukraine</e2>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Unlimited-classifier

With our unlimited-classifier you can use t5-for-ie to classify text into millions of categories. It applies generation with contraints that is super helful when structured and deterministic outputs are needed.

To install it, run the following command:

pip install -U unlimited-classifier

Right now you can try it with the following example:

from unlimited_classifier import TextClassifier

labels=[
        "e1 - capital of Ukraine",
        "e1 - capital of Poland",
        "e1 - European city",
        "e1 - Asian city",
        "e1 - small country"
    ]

classifier = TextClassifier(
    labels=['default'],
    model=model,
    tokenizer=tokenizer,
    device=device #if cuda 
)
classifier.initialize_labels_trie(labels)

text = "<e1>Kyiv</e1> is the capital <e2>Ukraine</e2>."

output = classifier.invoke(text)
print(output)

Turbo T5

We recommend to use this model on GPU with our TurboT5 package , it uses custom CUDA kernels that accelerate computations and allows much longer sequences.

First of all, you need to install the package

pip install turbot5 -U

Then you can import different heads for various purposes; we released more encoder heads for tasks such as token classification, question-answering or text classification and, of course, encoder-decoder heads for conditional generation:

from turbot5 import T5ForConditionalGeneration
from turbot5 import T5Config
from transformers import T5Tokenizer
import torch

tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
model = T5ForConditionalGeneration.from_pretrained("knowledgator/t5-for-ie",
 attention_type = 'flash', #put attention type you want to use
 use_triton=True).to('cuda')

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