Codeted Gailor in the analysis of the customer updates using an open source of IBM Ai Model Granite-3B and refreshes FACE Transformers

In this lesson, we will look at how we can be able to analyze the ideas easily in the text details using the Lodely Granite 3B Open 3B model integrated with Gugging Face Transformers. Feeling analysis, the most commonly used technological system (NLP), helps immediately reflect the sentiments expressed in the text. It makes it focus on businesses that aim to understand customer feedback and improve their products and services. Now, let's go with you by installing the required libraries, loading the IBM Granite model, distinguishes strips, and logical to your results, everything works well on Google Colab.

!pip install transformers torch accelerate

First, we will include important libraries – Transformers, Totorch, and fast – you need to load and operate NLP powerful models without seamless seamless models. Transformers provide for the prescribed NLP models, Torch works as a backend of deep learning activities, and acceleration guarantees the use of resources applicable to the GPUS.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import pandas as pd
import matplotlib.pyplot as plt

Then we will import the Python libraries. We will use the Torch of efficient functions, Transformers Uploading NLP models are trained from the surface, administrative pandas and processing data in your systematic and accurate process.

model_id = "ibm-granite/granite-3.0-3b-a800m-instruct"


tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='auto',
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)


generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

Here, we will upload the open IBM model for the next Granite 3B of Granite 3B, directly IBM-Granite / Granite-a800m, using the autootelformer's face. This model, the modeled education model is prepared to manage tasks such as directly within the colob separation within the colob, or under computational licensees.

def classify_sentiment(review):
    prompt = f"""Classify the sentiment of the following review as Positive, Negative, or Neutral.


Review: "{review}"


Sentiment:"""


    response = generator(
        prompt,
        max_new_tokens=5,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id
    )


    sentiment = response[0]['generated_text'].split("Sentiment:")[-1].split("n")[0].strip()
    return sentiment

Now we will specify the basic job of separation_s degree. This work sets the IBM Granite 3B model for the immediate use of the instructions of separating any reviews provided to be good, bad, or neutral. Format work format of installation, dragging model with specific instructions, and has issued emotions resulting in the text produced.

import pandas as pd


reviews = [
    "I absolutely loved the service! Definitely coming back.",
    "The item arrived damaged, very disappointed.",
    "Average product. Nothing too exciting.",
    "Superb experience, exceeded all expectations!",
    "Not worth the money, poor quality."
]


reviews_df = pd.DataFrame(reviews, columns=['review'])

Next, we will create a Datafame review update using the panda, containing a collection of example updates. These sampled reviews serve as sensitive classification details, enabling us to determine how much the IBM Granite model may determine the customer feelings.

reviews_df['sentiment'] = reviews_df['review'].apply(classify_sentiment)
print(reviews_df)

After explaining a review, we will use the Classify_s function to each review in the DataFram. This will generate a new column, emotions, when the IBM Granite model is planning each of the best, negative, or neutral. By printing updated review of the review of the reviewer_df, we can see the original text and its corresponding classification.

import matplotlib.pyplot as plt


sentiment_counts = reviews_df['sentiment'].value_counts()


plt.figure(figsize=(8, 6))
sentiment_counts.plot.pie(autopct="%1.1f%%", explode=[0.05]*len(sentiment_counts), colors=['#66bb6a', '#ff7043', '#42a5f5'])
plt.ylabel('')
plt.title('Sentiment Distribution of Reviews')
plt.show()

Finally, we will see the spread of feelings in a pie chart. This step gives a clear, accurate view that updates are divided, making interpreting the complete model function easier. The Mattplotlib is immediately allowing to see a part of the beautiful, unpleasant words, neutral stand, bring your mood for analysis.

In conclusion, use the Posincitive Adminination Pipeline using the IBM's Granite 3B Open-South Model held on the face of face. You have learned how to set training models before separating the text immediately, which is wrong, or neutral, visualize to understand successfully, and then translate the findings. This foundation option allows you to easily sync these skills to analyze datasets or inspect other NLP activities. Granite Models of IBM are combined with Gugging Face Transformers offering effective NLP tasks.

Here is the Colab Notebook. Also, don't forget to follow Sane and join ours Telegraph station including LinkedIn Grtopic. Don't forget to join ours 80k + ml subreddit.

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