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--- |
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language: |
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- en |
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tags: |
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- qa |
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- summarization |
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- emotion-detection |
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license: Apache 2.0 |
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datasets: |
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- coqa |
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- squad_v2 |
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- go_emotions |
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- cnn_dailymail |
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metrics: |
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- f1 |
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--- |
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# T5 Base with QA + Summary + Emotion |
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## Dependencies |
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Requires transformers>=4.0.0 |
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## Description |
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This model was finetuned on the CoQa, Squad 2, GoEmotions and CNN/DailyMail. |
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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. |
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Summarisation and emotion detection has not been evaluated yet. |
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## Usage |
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### Question answering |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion") |
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tokenizer = T5Tokenizer.from_pretrained("t5-base") |
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def get_answer(question, prev_qa, context): |
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input_text = [f"q: {qa[0]} a: {qa[1]}" for qa in prev_qa] |
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input_text.append(f"q: {question}") |
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input_text.append(f"c: {context}") |
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input_text = " ".join(input_text) |
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features = tokenizer([input_text], return_tensors='pt') |
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tokens = model.generate(input_ids=features['input_ids'], |
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attention_mask=features['attention_mask'], max_length=64) |
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return tokenizer.decode(tokens[0], skip_special_tokens=True) |
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print(get_answer("Why is the moon yellow?", "I'm not entirely sure why the moon is yellow.")) # unknown |
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context = "Elon Musk left OpenAI to avoid possible future conflicts with his role as CEO of Tesla." |
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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 |
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``` |
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### Summarisation |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion") |
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tokenizer = T5Tokenizer.from_pretrained("t5-base") |
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def summary(context): |
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input_text = f"summarize: {context}" |
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features = tokenizer([input_text], return_tensors='pt') |
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tokens = model.generate(input_ids=features['input_ids'], |
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attention_mask=features['attention_mask'], max_length=64) |
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return tokenizer.decode(tokens[0], skip_special_tokens=True) |
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``` |
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### Emotion detection |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion") |
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tokenizer = T5Tokenizer.from_pretrained("t5-base") |
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def emotion(context): |
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input_text = f"emotion: {context}" |
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features = tokenizer([input_text], return_tensors='pt') |
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tokens = model.generate(input_ids=features['input_ids'], |
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attention_mask=features['attention_mask'], max_length=64) |
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return tokenizer.decode(tokens[0], skip_special_tokens=True) |
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``` |
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