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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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
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"
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