Code for "How to get started" added

README.md

@@ -1,3 +1,162 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: text2text-generation
+tags:
+- legal
 ---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+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
+
+<!-- Provide a longer summary of what this model is. -->
+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
+<!-- - **Funded by [optional]:** [More Information Needed] -->
+<!-- - **Shared by [optional]:** [More Information Needed] -->
+- **Model type:** Language model
+- **Language(s) (NLP):** English
+<!-- - **License:** [More Information Needed] -->
+- **Finetuned from model [optional]:** [flan-t5-base](https://huggingface.co/google/flan-t5-base)
+
+
+## Uses
+
+
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Model is finetuned and can directly be used. 
+
+[More Information Needed]
+
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+
+## 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)
+
+
+```
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+