ojasaar commited on
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685bb05
1 Parent(s): 6514721

Update tokenizer

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  1. README.md +3 -3
README.md CHANGED
@@ -41,7 +41,7 @@ Summarisation and emotion detection has not been evaluated yet.
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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]
@@ -81,7 +81,7 @@ model.qa("Why not?", context, prev_qa=prev_qa)
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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}"
@@ -109,7 +109,7 @@ model.summarise("Long text to summarise")
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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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  ```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("kiri-ai/t5-base-qa-summary-emotion")
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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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  ```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("kiri-ai/t5-base-qa-summary-emotion")
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  def summary(context):
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  input_text = f"summarize: {context}"
 
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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("kiri-ai/t5-base-qa-summary-emotion")
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  def emotion(context):
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  input_text = f"emotion: {context}"