fea(examples): Updated the READMEs with requirements.txt installation

examples/audio-classification/README.md

@@ -20,6 +20,12 @@ The following examples showcase how to fine-tune `Wav2Vec2` for audio classifica
 
 Speech recognition models that have been pretrained in an unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), have shown to require only very little annotated data to yield good performance on speech classification datasets.
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
 
 ## Single-HPU
 

examples/contrastive-image-text/README.md

@@ -23,6 +23,13 @@ This folder contains two examples:
 
 Such models can be used for natural language image search and potentially zero-shot image classification.
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
+
 ## Download COCO dataset (2017)
 This example uses COCO dataset (2017) through a custom dataset script, which requires users to manually download the
 COCO dataset before training.

examples/image-classification/README.md

@@ -19,6 +19,13 @@ limitations under the License.
 This directory contains a script that showcases how to fine-tune any model supported by the [`AutoModelForImageClassification` API](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForImageClassification) (such as [ViT](https://huggingface.co/docs/transformers/main/en/model_doc/vit) or [Swin Transformer](https://huggingface.co/docs/transformers/main/en/model_doc/swin)) on HPUs. They can be used to fine-tune models on both [datasets from the hub](#using-datasets-from-hub) as well as on [your own custom data](#using-your-own-data).
 
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
+
 ## Single-HPU training
 
 ### Using datasets from Hub

examples/language-modeling/README.md

@@ -22,6 +22,13 @@ GPT-2 is trained or fine-tuned using a causal language modeling (CLM) loss while
 The following examples will run on datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
 text files for training and validation. We give examples of both below.
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
+
 ## GPT2/GPT-J/GPT-NeoX and causal language modeling
 
 The following examples fine-tune GPT-2, GPT-J-6B and GPT-NeoX-20B on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before the tokenization). The loss here is the one of causal language modeling.

examples/protein-folding/README.md

@@ -34,6 +34,13 @@ The predicted protein structure will be stored in save-hpu.pdb file. We can use
 
 # Mila-Intel protST example
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
+
 ## Single-HPU inference for zero shot evaluation
 Here we show how to run zero shot evaluation of protein ST model on HPU:
 

examples/question-answering/README.md

@@ -26,6 +26,13 @@ uses special features of those tokenizers. You can check if your favorite model
 
 Note that if your dataset contains samples with no possible answers (like SQUAD version 2), you need to pass along the flag `--version_2_with_negative`.
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
+
 ## Fine-tuning BERT on SQuAD1.1
 
 For the following cases, an example of a Gaudi configuration file is given

examples/speech-recognition/README.md

@@ -27,6 +27,13 @@ limitations under the License.
 	- [Inference](#single-hpu-seq2seq-inference)
 
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
+
 ## Connectionist Temporal Classification
 
 The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/optimum-habana/tree/main/examples/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForCTC) for automatic speech recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.

examples/summarization/README.md

@@ -23,6 +23,13 @@ This directory contains examples for finetuning and evaluating transformers on s
 For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets#json-files.
 You will also find examples of these below.
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
+
 ## Single-card Training
 
 Here is an example of a summarization task with T5:

examples/text-classification/README.md

@@ -27,6 +27,12 @@ and can also be used for a dataset hosted on our [hub](https://huggingface.co/da
 
 GLUE is made up of a total of 9 different tasks where the task name can be cola, sst2, mrpc, stsb, qqp, mnli, qnli, rte or wnli.
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
 
 ## Fine-tuning BERT on MRPC
 

examples/text-to-speech/README.md

@@ -18,6 +18,13 @@ limitations under the License.
 
 This directory contains a script that showcases how to use the Transformers pipeline API to run text to speech task on HPUs.
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
+
 ## Single-HPU inference
 
 ```bash

examples/translation/README.md

@@ -21,6 +21,12 @@ limitations under the License.
 For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets#json-files.
 You will also find examples of these below.
 
+## Requirements
+
+First, you should install the requirements:
+```bash
+pip install -r requirements.txt
+```
 
 ## Single-card Training
 

examples/trl/README.md

@@ -1,10 +1,9 @@
 # Examples
 
 
-## Prerequisites
-
-Install all the dependencies in the `requirements.txt`:
+## Requirements
 
+First, you should install the requirements:
 ```
 $ pip install -U -r requirements.txt
 ```
@@ -266,4 +265,4 @@ results = pipeline(prompts)
 
 for prompt, image in zip(prompts, results.images):
     image.save(f"{prompt}.png")
-```
+```