Update README.md

README.md

@@ -71,78 +71,6 @@ where
 `N` is the number of words in the reference (`N=S+D+C`).
 
 
-## How to use 
-
-The metric takes two inputs: references (a list of references for each speech input) and predictions (a list of transcriptions to score).
-
-
-```python
-from evaluate import load
-wer = load("wer")
-wer_score = wer.compute(predictions=predictions, references=references)
-```
-## Output values
-
-This metric outputs a float representing the word error rate.
-
-```
-print(wer_score)
-0.5
-```
-
-This value indicates the average number of errors per reference word. 
-
-The **lower** the value, the **better** the performance of the ASR system, with a WER of 0 being a perfect score.
-
-### Values from popular papers
-
-This metric is highly dependent on the content and quality of the dataset, and therefore users can expect very different values for the same model but on different datasets.
-
-For example, datasets such as [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) report a WER in the 1.8-3.3 range, whereas ASR models evaluated on [Timit](https://huggingface.co/datasets/timit_asr) report a WER in the 8.3-20.4 range. 
-See the leaderboards for [LibriSpeech](https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean) and [Timit](https://paperswithcode.com/sota/speech-recognition-on-timit) for the most recent values.
-
-## Examples 
-
-Perfect match between prediction and reference:
-
-```python
-from evaluate import load
-wer = load("wer")
-predictions = ["hello world", "good night moon"]
-references = ["hello world", "good night moon"]
-wer_score = wer.compute(predictions=predictions, references=references)
-print(wer_score)
-0.0
-```
-
-Partial match between prediction and reference:
-
-```python
-from evaluate import load
-wer = load("wer")
-predictions = ["this is the prediction", "there is an other sample"]
-references = ["this is the reference", "there is another one"]
-wer_score = wer.compute(predictions=predictions, references=references)
-print(wer_score)
-0.5
-```
-
-No match between prediction and reference:
-
-```python
-from evaluate import load
-wer = load("wer")
-predictions = ["hello world", "good night moon"]
-references = ["hi everyone", "have a great day"]
-wer_score = wer.compute(predictions=predictions, references=references)
-print(wer_score)
-1.0
-```
-
-## Limitations and bias
-
-WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort. 
-
 ## Citation
 
 ```bibtex