Hello,

Going via the training. Some small ideas for improvements.

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Transformers, what can they do? https://huggingface.co/learn/nlp-course/en/chapter1/3

A) Current code sample is incomplete

from transformers import pipeline

classifier = pipeline("sentiment-analysis") classifier("I've been waiting for a HuggingFace course my whole life.")

CORRECT COULD BE B) from transformers import pipeline

classifier = pipeline("sentiment-analysis") result = classifier("I've been waiting for a HuggingFace course my whole life.") print(result)

C) Even better could be

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Suppresses TensorFlow logs os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' # Disables oneDNN custom operations

from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification import warnings import torch

import tensorflow as tf tf.get_logger().setLevel('ERROR')

Set environment variable to disable oneDNN custom operations warning (specific to TensorFlow)

os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'

Suppress warnings

warnings.filterwarnings('ignore', category=DeprecationWarning)

Check if a GPU is available

device = 0 if torch.cuda.is_available() else -1

Load the tokenizer with the clean_up_tokenization_spaces parameter set

tokenizer = AutoTokenizer.from_pretrained( "distilbert/distilbert-base-uncased-finetuned-sst-2-english", clean_up_tokenization_spaces=True )

Load the model in PyTorch

model = AutoModelForSequenceClassification.from_pretrained( "distilbert/distilbert-base-uncased-finetuned-sst-2-english" )

Initialize the sentiment-analysis pipeline with the custom tokenizer and PyTorch model

classifier = pipeline( "sentiment-analysis", model=model, framework='pt', # Use PyTorch tokenizer=tokenizer, device=device # Use GPU if available, otherwise use CPU )

result = classifier( ["I've been waiting for a HuggingFace course my whole life.", "I hate this so much!"] )

print(result)

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https://huggingface.co/learn/nlp-course/en/chapter8/5 transformers-cli env

transformers version: 4.44.0
Platform: Windows-10-10.0.22631-SP0
Python version: 3.11.9
Huggingface_hub version: 0.24.5
Safetensors version: 0.4.4
Accelerate version: 0.33.0
Accelerate config: not found
PyTorch version (GPU?): 2.4.0+cu118 (True)
Tensorflow version (GPU?): 2.17.0 (False)
Flax version (CPU?/GPU?/TPU?): 0.7.0 (cpu)
Jax version: 0.4.13
JaxLib version: 0.4.13
Using distributed or parallel set-up in the script?:
Using GPU in script?:
GPU type: NVIDIA GeForce RTX 3060 Laptop GPU

huggingface / course

Transformers, what can they do? #718