Update README.md

README.md

@@ -11,7 +11,7 @@ metrics:
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-We use Mix Thoughts Distillation to distill mathematical reasoning ability from gpt-3.5-turbo to CodeT5+-770m-py.
+We use Ensemble Thoughts Distillation to distill mathematical reasoning ability from gpt-3.5-turbo to CodeT5+-770m-py.
 
 ### Model Description
 
@@ -47,7 +47,7 @@ When given a question, the prompt "System of linear equations: (Do not simplify)
 
 ```python
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-checkpoint = "zhuxunyu/mtd-codet5p-770m-py"
+checkpoint = "zhuxunyu/etd-codet5p-770m-py"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint).to(device)
@@ -62,7 +62,7 @@ generation = tokenizer.decode(output, skip_special_tokens=True)
 
 ```python
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-checkpoint = "zhuxunyu/mtd-codet5p-770m-py"
+checkpoint = "zhuxunyu/etd-codet5p-770m-py"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint).to(device)
@@ -76,7 +76,7 @@ generation = tokenizer.decode(output, skip_special_tokens=True)
 
 ```python
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-checkpoint = "zhuxunyu/mtd-codet5p-770m-py"
+checkpoint = "zhuxunyu/etd-codet5p-770m-py"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint).to(device)
@@ -111,7 +111,7 @@ their corresponding reasoning processes are built as a training dataset, and we
 |   PoT   | 50.34 | 55.2  | 51.6  |   88.33    |
 |   EoT   | 48.21 | 52.81 | 55.7  |   70.16    |
 |   CoT   | 25.47 | 29.67 | 23.3  |    46.5    |
-| Mix_all | 50.56 | 55.34 | 52.3  |   88.83    |
+| Ensemble_all | 50.56 | 55.34 | 52.3  |   88.83    |