Update README.md

README.md

@@ -15,31 +15,24 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This is the model used to seperate long questions into seperate questions if possible.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** [Geerath Bhat]
+- **Funded by [optional]:** [Geerath Bhat]
+- **Shared by [optional]:** [Geerath Bhat]
+- **Model type:** [Fine-tuned Instruct LLM]
+- **Language(s) (NLP):** [English]
+- **License:** []
+- **Finetuned from model [optional]:** []
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+We can use this model to seperate long context data into seperate meaningful parts.
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+You can use this model and soon we will provide a demo version also.
 
 [More Information Needed]
 
@@ -57,143 +50,130 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model will not work in very complex situations but we have measured it's perfommance and it performs well on most complex tasks.
 
 [More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Give a complex nested questions and it will seperate those questions or contexr into meaningful parts.
 
 Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
+  ```
+  from transformers import AutoModelForSeq2SeqLM
+  import nltk
+  nltk.download('punkt')
+  nltk.download('punkt_tab')
+  import string
+  from transformers import AutoTokenizer
+```
+
+# Get the tokenizer
+
+```
+model_checkpoint = "Geerath/seperator"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+```
+
+# Load the model
+```
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+max_input_length = 512
+```
+## Inference
+```
+prompt = """
+You are given a query that combines multiple questions into a single string. Your task is to break down this combined query into individual questions, ensuring each question is clear and stands alone."""
+text = """
+Where is IISc Located, what is GDP?, How can we utilize the power of wind mill and what is photosynthesis?
+"""
+
+inputs = [prompt + text]
+
+inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt")
+output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=512)
+decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+predicted_title = nltk.sent_tokenize(decoded_output.strip())
+
+print(predicted_title)
+```
+## Result - ['Where is IISc Located, what is GDP?, How can we utilize the power of wind mill, and what is photosynthesis?']
 
 ### 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]
+Custom dataset generated using multiple LLMs.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Finetuned T5 on custom dataset
 
 #### Preprocessing [optional]
-
-[More Information Needed]
-
+```
+max_input_length = 512
+max_target_length = 512
+def clean_text(text):
+  sentences = nltk.sent_tokenize(text.strip())
+  sentences_cleaned = [s for sent in sentences for s in sent.split("\n")]
+  sentences_cleaned_no_titles = [sent for sent in sentences_cleaned
+                                 if len(sent) > 0 and
+                                 sent[-1] in string.punctuation]
+  text_cleaned = "\n".join(sentences_cleaned_no_titles)
+  return text_cleaned
+
+def preprocess_data(examples):
+  texts_cleaned = [text for text in examples["input"]]
+  inputs = [prefix + text for text in texts_cleaned]
+  model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)
+  with tokenizer.as_target_tokenizer():
+    labels = tokenizer(examples["output"], max_length=max_target_length,
+                       truncation=True)
+
+  model_inputs["labels"] = labels["input_ids"]
+  return model_inputs
+```
 
 #### 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]
+```
+batch_size = 16
+args = Seq2SeqTrainingArguments(
+    model_dir,
+    evaluation_strategy="steps",
+    eval_steps=100,
+    logging_strategy="steps",
+    logging_steps=100,
+    save_strategy="steps",
+    save_steps=200,
+    learning_rate=4e-5,
+    per_device_train_batch_size=batch_size,
+    per_device_eval_batch_size=batch_size,
+    weight_decay=0.01,
+    save_total_limit=3,
+    num_train_epochs=10,
+    predict_with_generate=True,
+    fp16=True,
+    load_best_model_at_end=True,
+    metric_for_best_model="rouge1",
+    #push_to_hub=True
+)
+```
 
 ## 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]
+Custom data prepared.
 
-#### 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
 
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[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]
+Model take a input as a text and gives output as list of text seperated by commas.
+Example - Input -> Where is IISc Located, what is GDP?, How can we utilize the power of wind mill and what is photosynthesis?
+          Output -> ['Where is IISc Located, what is GDP?, How can we utilize the power of wind mill, and what is photosynthesis?']