license: mit
language:
- en
pipeline_tag: text2text-generation
tags:
- legal
Model Card for Model ID
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
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
- Model type: Language model
- Language(s) (NLP): English
- Finetuned from model [optional]: flan-t5-base
Uses
Direct Use
Model is finetuned and can directly be used.
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Recommendations
How to Get Started with the Model
Use the code below to get started with the model.
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
Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Technical Specifications [optional]
Model Architecture and Objective
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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