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---
language:
- en
tags:
- qa
- summarization
- emotion-detection
license: Apache 2.0
datasets:
- coqa
- squad_v2
- go_emotions
- cnn_dailymail
metrics:
- f1
---
# 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
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("t5-base")
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
```
### Summarisation
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("t5-base")
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)
```
### Emotion detection
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("t5-base")
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)
```
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