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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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tags: |
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- legal |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is useful in the pipeline of complex information extraction. The model will generate discourse trees from complex sentences. |
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Discourse trees contain simple split sentences and relationship between these sentences. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model is useful in the pipeline of complex information extraction. The model will generate discourse trees from complex sentences. |
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Discourse trees contain simple split sentences and relationship between these sentences. |
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- **Developed by:** BITS Hyderabad |
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<!-- - **Funded by [optional]:** [More Information Needed] --> |
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<!-- - **Shared by [optional]:** [More Information Needed] --> |
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- **Model type:** Language model |
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- **Language(s) (NLP):** English |
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<!-- - **License:** [More Information Needed] --> |
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- **Finetuned from model [optional]:** [flan-t5-base](https://huggingface.co/google/flan-t5-base) |
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## Uses |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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Model is finetuned and can directly be used. |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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# If using Google Colab, login to HuggingFace is needed. Doing the following will prompt to enter the access token |
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# which can be obtained from Settings > AccessTokens |
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from huggingface_hub import notebook_login |
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notebook_login() |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import spacy |
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tokenizer = AutoTokenizer.from_pretrained("bphclegalie/t5-base-legen", token = True) |
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model = AutoModelForSeq2SeqLM.from_pretrained("bphclegalie/t5-base-legen", token = True) |
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nlp = spacy.load("en_core_web_sm") |
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def get_discourse_tree(text): |
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sentences = " ".join([t.text for t in nlp(text)]) |
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input_ids = tokenizer(text, max_length=384, truncation=True, return_tensors="pt").input_ids |
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outputs = model.generate(input_ids=input_ids, max_length=128) |
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answer = [tokenizer.decode(output, skip_special_tokens = True) for output in outputs] |
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return " ".join(answer) |
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``` |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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