t5-base-legen

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This model is useful in the pipeline of complex information extraction. The model will generate discourse trees from complex sentences. Discourse trees contain simple split sentences and relationship between these sentences.

Model Details

Model Description

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This model is useful in the pipeline of complex information extraction. The model will generate discourse trees from complex sentences. Discourse trees contain simple split sentences and relationship between these sentences.

• Developed by: BITS Hyderabad

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• Model type: Language model
• Language(s) (NLP): English

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• Finetuned from model [optional]: flan-t5-base

Uses

Direct Use

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Model is finetuned and can directly be used.

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Recommendations

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How to Get Started with the Model

Use the code below to get started with the model.

# If using Google Colab, login to HuggingFace is needed. Doing the following will prompt to enter the access token
# which can be obtained from Settings > AccessTokens 
from huggingface_hub import notebook_login

notebook_login()


from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import spacy

tokenizer = AutoTokenizer.from_pretrained("bphclegalie/t5-base-legen", token = True)
model = AutoModelForSeq2SeqLM.from_pretrained("bphclegalie/t5-base-legen", token = True)

nlp = spacy.load("en_core_web_sm")


def get_discourse_tree(text):
  sentences = " ".join([t.text for t in nlp(text)])

  input_ids = tokenizer(text, max_length=384, truncation=True, return_tensors="pt").input_ids
  outputs = model.generate(input_ids=input_ids, max_length=128)

  answer = [tokenizer.decode(output, skip_special_tokens = True) for output in outputs]
  return " ".join(answer)

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

Technical Specifications [optional]

Model Architecture and Objective

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