materials first commit

Author: hfhoffman1144

huggingface-transformers/README.md

@@ -0,0 +1,24 @@
+# HuggingFace Transformers: Leverage Open-Source AI in Python
+
+This repo contains the materials for the tutorial (HuggingFace Transformers: Leverage Open-Source AI in Python)[https://realpython.com/huggingface-transformers-open-source-ai-in-python/].
+
+Transformers is available on [PyPI](https://pypi.org/), and you can install it with [pip](https://realpython.com/what-is-pip/). Open a terminal or command prompt, create a new virtual environment, and then run the following command:
+
+```console
+(venv) $ python -m pip install transformers
+```
+
+This command will install the latest version of Transformers from PyPI onto your machine. Throughout this tutorial, you'll also leverage [Pytorch](https://realpython.com/pytorch-vs-tensorflow/) to interact with models at a lower level. You can install Pytorch with the following command:
+
+```console
+(venv) $ python -m pip install torch
+```
+
+To verify that the installations were successful, start a [Python REPL](https://realpython.com/python-repl/) and import `transformers` and `torch`:
+
+```pycon
+>>> import transformers
+>>> import torch
+```
+
+If the imports run without errors, then you've successfully installed the dependencies needed for this tutorial.

huggingface-transformers/auto_classes.py

@@ -2,11 +2,12 @@
 from transformers import (
     AutoTokenizer,
     AutoModelForSequenceClassification,
-    pipeline,
+    AutoConfig,
 )
 
 model_name = "cardiffnlp/twitter-roberta-base-sentiment-latest"
 
+config = AutoConfig.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
@@ -18,10 +19,7 @@
 
 scores = output.logits[0]
 probabilities = torch.softmax(scores, dim=0)
-predicted_class = probabilities.argmax().item()
 
-print(f"Predicted class: {predicted_class}")
-print(f"Probabilities: {probabilities.tolist()}")
-
-full_pipeline = pipeline(model=model_name)
-print(full_pipeline(text))
+for i, prob in enumerate(probabilities):
+    label = config.id2label[i]
+    print(f"{i + 1}) {label}: {prob}")