Update README

README.md

@@ -47,9 +47,9 @@ import torch
 from chronos import ChronosPipeline
 
 pipeline = ChronosPipeline.from_pretrained(
-  "amazon/chronos-t5-small",
-  device_map="cuda",
-  torch_dtype=torch.bfloat16,
+    "amazon/chronos-t5-small",
+    device_map="cuda",
+    torch_dtype=torch.bfloat16,
 )
 
 df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv")
@@ -59,12 +59,12 @@ df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnal
 context = torch.tensor(df["#Passengers"])
 prediction_length = 12
 forecast = pipeline.predict(
-  context,
-  prediction_length,
-  num_samples=20,
-  temperature=1.0,
-  top_k=50,
-  top_p=1.0,
+    context,
+    prediction_length,
+    num_samples=20,
+    temperature=1.0,
+    top_k=50,
+    top_p=1.0,
 ) # forecast shape: [num_series, num_samples, prediction_length]
 
 # visualize the forecast
@@ -90,17 +90,17 @@ import torch
 from chronos import ChronosPipeline
 
 pipeline = ChronosPipeline.from_pretrained(
-  "amazon/chronos-t5-small",
-  device_map="cuda",
-  torch_dtype=torch.bfloat16,
+    "amazon/chronos-t5-small",
+    device_map="cuda",
+    torch_dtype=torch.bfloat16,
 )
 
 df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv")
 
 # context must be either a 1D tensor, a list of 1D tensors,
 # or a left-padded 2D tensor with batch as the first dimension
 context = torch.tensor(df["#Passengers"])
-embeddings, decoding_context = pipeline.embed(context)
+embeddings, tokenizer_state = pipeline.embed(context)
 ```