T5 Base Qa Summary Emotion

🚀 T5 Base with QA + Summary + Emotion

This model combines question answering, summarization, and emotion detection capabilities. It is fine - tuned on multiple datasets to provide high - performance text - to - text generation.

🚀 Quick Start

This section provides a quick overview of the model's dependencies and what it can do.

✨ Features

• Multi - task Capability: Supports question answering, summarization, and emotion detection.
• High Performance: Achieves high F1 scores on question - answering datasets like Squad 2 and CoQa.

📦 Installation

Dependencies

Requires transformers>=4.0.0

💻 Usage Examples

Basic Usage

This model can be used for different tasks such as question answering, summarization, and emotion detection.

Advanced Usage

Here are detailed usage examples for each task.

Question answering

With Transformers

from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")

def get_answer(question, prev_qa, context):
    input_text = [f"q: {qa[0]} a: {qa[1]}" for qa in prev_qa]
    input_text.append(f"q: {question}")
    input_text.append(f"c: {context}")
    input_text = " ".join(input_text)
    features = tokenizer([input_text], return_tensors='pt')
    tokens = model.generate(input_ids=features['input_ids'], 
            attention_mask=features['attention_mask'], max_length=64)
    return tokenizer.decode(tokens[0], skip_special_tokens=True)

print(get_answer("Why is the moon yellow?", "I'm not entirely sure why the moon is yellow.")) # unknown

context = "Elon Musk left OpenAI to avoid possible future conflicts with his role as CEO of Tesla."

print(get_answer("Why not?", [("Does Elon Musk still work with OpenAI", "No")], context)) # to avoid possible future conflicts with his role as CEO of Tesla

With Kiri

from kiri.models import T5QASummaryEmotion

context = "Elon Musk left OpenAI to avoid possible future conflicts with his role as CEO of Tesla."
prev_qa = [("Does Elon Musk still work with OpenAI", "No")]
model = T5QASummaryEmotion()

# Leave prev_qa blank for non conversational question-answering
model.qa("Why not?", context, prev_qa=prev_qa)
> "to avoid possible future conflicts with his role as CEO of Tesla"

Summarisation

With Transformers

from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")

def summary(context):
    input_text = f"summarize: {context}"
    features = tokenizer([input_text], return_tensors='pt')
    tokens = model.generate(input_ids=features['input_ids'], 
            attention_mask=features['attention_mask'], max_length=64)
    return tokenizer.decode(tokens[0], skip_special_tokens=True)

With Kiri

from kiri.models import T5QASummaryEmotion

model = T5QASummaryEmotion()

model.summarise("Long text to summarise")
> "Short summary of long text"

Emotion detection

With Transformers

from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")

def emotion(context):
    input_text = f"emotion: {context}"
    features = tokenizer([input_text], return_tensors='pt')
    tokens = model.generate(input_ids=features['input_ids'], 
            attention_mask=features['attention_mask'], max_length=64)
    return tokenizer.decode(tokens[0], skip_special_tokens=True)

With Kiri

from kiri.models import T5QASummaryEmotion

model = T5QASummaryEmotion()

model.emotion("I hope this works!")
> "optimism"

📚 Documentation

Description

This model was finetuned on the CoQa, Squad 2, GoEmotions and CNN/DailyMail.

It achieves a score of F1 79.5 on the Squad 2 dev set and a score of F1 70.6 on the CoQa dev set.

Summarisation and emotion detection has not been evaluated yet.

Information Table

📄 License

This project is licensed under the Apache 2.0 license.

About us

Kiri makes using state - of - the - art models easy, accessible and scalable.

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