# T5 Base with QA + Summary + Emotion

## Dependencies

Requires transformers>=4.0.0

## 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.

## Usage

### Question answering

#### With Transformers

```python
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

```python
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

```python
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

```python
from kiri.models import T5QASummaryEmotion

model = T5QASummaryEmotion()

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

### Emotion detection

#### With Transformers

```python
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

```python
from kiri.models import T5QASummaryEmotion

model = T5QASummaryEmotion()

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

## About us

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

[Website](https://kiri.ai) | [Natural Language Engine](https://github.com/kiri-ai/kiri)

{"language": ["en"], "license": "apache-2.0", "tags": ["question-answering", "emotion-detection", "summarisation"], "datasets": ["coqa", "squad_v2", "go_emotions", "cnn_dailymail"], "metrics": ["f1"], "pipeline_tag": "text2text-generation", "widget": [{"text": "q: Who is Elon Musk? a: an entrepreneur q: When was he born? c: Elon Musk is an entrepreneur born in 1971. </s>"}, {"text": "emotion: I hope this works! </s>"}]}
transformers pytorch t5 text2text-generation question-answering emotion-detection summarisation en dataset:coqa dataset:squad_v2 dataset:go_emotions dataset:cnn_dailymail license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #question-answering #emotion-detection #summarisation #en #dataset-coqa #dataset-squad_v2 #dataset-go_emotions #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# T5 Base with QA + Summary + Emotion

## Dependencies

Requires transformers>=4.0.0

## 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.

## Usage

### Question answering

#### With Transformers



#### With Kiri



### Summarisation

#### With Transformers



#### With Kiri



### Emotion detection

#### With Transformers



#### With Kiri



## About us

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

Website | Natural Language Engine

# T5 Base with QA + Summary + Emotion ## Dependencies

Requires transformers>=4.0.0 ## 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. ## Usage ### Question answering #### With Transformers #### With Kiri ### Summarisation #### With Transformers #### With Kiri ### Emotion detection #### With Transformers #### With Kiri ## About us

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

Website | Natural Language Engine
TAGS
#transformers #pytorch #t5 #text2text-generation #question-answering #emotion-detection #summarisation #en #dataset-coqa #dataset-squad_v2 #dataset-go_emotions #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # T5 Base with QA + Summary + Emotion ## Dependencies

Requires transformers>=4.0.0 ## 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. ## Usage ### Question answering #### With Transformers #### With Kiri ### Summarisation #### With Transformers #### With Kiri ### Emotion detection #### With Transformers #### With Kiri ## About us

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

Website | Natural Language Engine
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #question-answering #emotion-detection #summarisation #en #dataset-coqa #dataset-squad_v2 #dataset-go_emotions #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# T5 Base with QA + Summary + Emotion## Dependencies

Requires transformers>=4.0.0## 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.## Usage### Question answering#### With Transformers#### With Kiri### Summarisation#### With Transformers#### With Kiri### Emotion detection#### With Transformers#### With Kiri## About us

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

Website | Natural Language Engine