update readme

README.md

@@ -16,6 +16,10 @@ metrics:
 ---
 # 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.

README.md~

@@ -1,81 +0,0 @@
----
-language: 
-- en
-tags:
-- question-answering
-- summarization
-- emotion-detection
-license: Apache 2.0
-datasets:
-- coqa
-- squad_v2
-- go_emotions
-- cnn_dailymail
-metrics:
-- f1
----
-# T5 Base with QA + Summary + Emotion
-
-## Description
-
-This model was finetuned on the CoQa, Squad 2, GoEmotions and CNN/DailyMail.
-
-It achieves a score of **F1 76.7** on the Squad 2 dev set and a score of **F1 68.5** 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)
-```