let -> const

docs/source/pipelines.md

@@ -16,7 +16,7 @@ Start by creating an instance of `pipeline()` and specifying a task you want to
 ```javascript
 import { pipeline } from '@huggingface/transformers';
 
-let classifier = await pipeline('sentiment-analysis');
+const classifier = await pipeline('sentiment-analysis');
 ```
 
 When running for the first time, the `pipeline` will download and cache the default pretrained model associated with the task. This can take a while, but subsequent calls will be much faster.
@@ -30,14 +30,14 @@ By default, models will be downloaded from the [Hugging Face Hub](https://huggin
 You can now use the classifier on your target text by calling it as a function:
 
 ```javascript
-let result = await classifier('I love transformers!');
+const result = await classifier('I love transformers!');
 // [{'label': 'POSITIVE', 'score': 0.9998}]
 ```
 
 If you have multiple inputs, you can pass them as an array:
 
 ```javascript
-let result = await classifier(['I love transformers!', 'I hate transformers!']);
+const result = await classifier(['I love transformers!', 'I hate transformers!']);
 // [{'label': 'POSITIVE', 'score': 0.9998}, {'label': 'NEGATIVE', 'score': 0.9982}]
 ```
 
@@ -46,9 +46,9 @@ You can also specify a different model to use for the pipeline by passing it as
 <!-- TODO: REPLACE 'nlptown/bert-base-multilingual-uncased-sentiment' with 'nlptown/bert-base-multilingual-uncased-sentiment'-->
 
 ```javascript
-let reviewer = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
+const reviewer = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
 
-let result = await reviewer('The Shawshank Redemption is a true masterpiece of cinema.');
+const result = await reviewer('The Shawshank Redemption is a true masterpiece of cinema.');
 // [{label: '5 stars', score: 0.8167929649353027}]
 ```
 
@@ -59,10 +59,10 @@ The `pipeline()` function is a great way to quickly use a pretrained model for i
 <!-- TODO: Replace 'Xenova/whisper-small.en' with 'openai/whisper-small.en' -->
 ```javascript
 // Allocate a pipeline for Automatic Speech Recognition
-let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small.en');
+const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small.en');
 
 // Transcribe an audio file, loaded from a URL.
-let result = await transcriber('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac');
+const result = await transcriber('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac');
 // {text: ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
 ```
 
@@ -86,7 +86,7 @@ You can also specify which revision of the model to use, by passing a `revision`
 Since the Hugging Face Hub uses a git-based versioning system, you can use any valid git revision specifier (e.g., branch name or commit hash)
 
 ```javascript
-let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en', {
+const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en', {
     revision: 'output_attentions',
 });
 ```
@@ -100,17 +100,17 @@ Many pipelines have additional options that you can specify. For example, when u
 <!-- TODO: Replace 'Xenova/nllb-200-distilled-600M' with 'facebook/nllb-200-distilled-600M' -->
 ```javascript
 // Allocation a pipeline for translation
-let translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
+const translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
 
 // Translate from English to Greek
-let result = await translator('I like to walk my dog.', {
+const result = await translator('I like to walk my dog.', {
     src_lang: 'eng_Latn',
     tgt_lang: 'ell_Grek'
 });
 // [ { translation_text: 'Μου αρέσει να περπατάω το σκυλί μου.' } ]
 
 // Translate back to English
-let result2 = await translator(result[0].translation_text, {
+const result2 = await translator(result[0].translation_text, {
     src_lang: 'ell_Grek',
     tgt_lang: 'eng_Latn'
 });
@@ -125,8 +125,8 @@ For example, to generate a poem using `LaMini-Flan-T5-783M`, you can do:
 
 ```javascript
 // Allocate a pipeline for text2text-generation
-let poet = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
-let result = await poet('Write me a love poem about cheese.', {
+const poet = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
+const result = await poet('Write me a love poem about cheese.', {
     max_new_tokens: 200,
     temperature: 0.9,
     repetition_penalty: 2.0,