Update Space (evaluate main: 9be38a7c)

README.md

@@ -37,13 +37,13 @@ The metric takes a list of text as input, as well as the name of the model used
 ```python
 from evaluate import load
 perplexity = load("perplexity", module_type="metric")
-results = perplexity.compute(input_texts=input_texts, model_id='gpt2')
+results = perplexity.compute(predictions=predictions, model_id='gpt2')
 ```
 
 ### Inputs
 - **model_id** (str): model used for calculating Perplexity. NOTE: Perplexity can only be calculated for causal language models.
     - This includes models such as gpt2, causal variations of bert, causal versions of t5, and more (the full list can be found in the AutoModelForCausalLM documentation here: https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
-- **input_texts** (list of str): input text, each separate text snippet is one list entry.
+- **predictions** (list of str): input text, each separate text snippet is one list entry.
 - **batch_size** (int): the batch size to run texts through the model. Defaults to 16.
 - **add_start_token** (bool): whether to add the start token to the texts, so the perplexity can include the probability of the first word. Defaults to True.
 - **device** (str): device to run on, defaults to 'cuda' when available
@@ -62,13 +62,13 @@ This metric's range is 0 and up. A lower score is better.
 
 
 ### Examples
-Calculating perplexity on input_texts defined here:
+Calculating perplexity on predictions defined here:
 ```python
 perplexity = evaluate.load("perplexity", module_type="metric")
 input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
 results = perplexity.compute(model_id='gpt2',
                              add_start_token=False,
-                             input_texts=input_texts)
+                             predictions=input_texts)
 print(list(results.keys()))
 >>>['perplexities', 'mean_perplexity']
 print(round(results["mean_perplexity"], 2))
@@ -76,7 +76,7 @@ print(round(results["mean_perplexity"], 2))
 print(round(results["perplexities"][0], 2))
 >>>11.11
 ```
-Calculating perplexity on input_texts loaded in from a dataset:
+Calculating perplexity on predictions loaded in from a dataset:
 ```python
 perplexity = evaluate.load("perplexity", module_type="metric")
 input_texts = datasets.load_dataset("wikitext",
@@ -84,7 +84,7 @@ input_texts = datasets.load_dataset("wikitext",
                                     split="test")["text"][:50]
 input_texts = [s for s in input_texts if s!='']
 results = perplexity.compute(model_id='gpt2',
-                             input_texts=input_texts)
+                             predictions=input_texts)
 print(list(results.keys()))
 >>>['perplexities', 'mean_perplexity']
 print(round(results["mean_perplexity"], 2))

perplexity.py

@@ -43,7 +43,7 @@ Args:
                     in the AutoModelForCausalLM documentation here:
                     https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
 
-    input_texts (list of str): input text, each separate text snippet
+    predictions (list of str): input text, each separate text snippet
         is one list entry.
     batch_size (int): the batch size to run texts through the model. Defaults to 16.
     add_start_token (bool): whether to add the start token to the texts,
@@ -60,7 +60,7 @@ Examples:
         >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
         >>> results = perplexity.compute(model_id='gpt2',
         ...                              add_start_token=False,
-        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
+        ...                              predictions=input_texts) # doctest:+ELLIPSIS
         >>> print(list(results.keys()))
         ['perplexities', 'mean_perplexity']
         >>> print(round(results["mean_perplexity"], 2))
@@ -74,7 +74,7 @@ Examples:
         >>> input_texts = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")["text"][:10] # doctest: +SKIP
         >>> input_texts = [s for s in input_texts if s!='']
         >>> results = perplexity.compute(model_id='gpt2',
-        ...                              input_texts=input_texts)
+        ...                              predictions=input_texts)
         >>> print(list(results.keys()))
         ['perplexities', 'mean_perplexity']
         >>> print(round(results["mean_perplexity"], 2)) # doctest: +SKIP
@@ -94,13 +94,13 @@ class Perplexity(evaluate.EvaluationModule):
             inputs_description=_KWARGS_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "input_texts": datasets.Value("string"),
+                    "predictions": datasets.Value("string"),
                 }
             ),
             reference_urls=["https://huggingface.co/docs/transformers/perplexity"],
         )
 
-    def _compute(self, input_texts, model_id, batch_size: int = 16, add_start_token: bool = True, device=None):
+    def _compute(self, predictions, model_id, batch_size: int = 16, add_start_token: bool = True, device=None):
 
         if device is not None:
             assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
@@ -136,7 +136,7 @@ class Perplexity(evaluate.EvaluationModule):
             max_tokenized_len = model.config.max_length
 
         encodings = tokenizer(
-            input_texts,
+            predictions,
             add_special_tokens=False,
             padding=True,
             truncation=True,