#textdataset

[globaltechnologysolution]

Unlock insights and unleash the power of your data with our comprehensive text dataset for machine learning.

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www.gta.ai

[aibyrdidini]

Expanding Horizons: AI's Diverse Applications in Daily Tasks.

Generative AI typically provides solutions for writing, reading, and chatting, yet its scope extends far beyond. Continuously progressing in areas like images, predictions, voice and image recognition, data analysis, graphs, statistics, maps, and more, it addresses a wide range of tasks that align with human intelligence. This evolution positions AI as a dynamic tool enhancing everyday activities.

Here's a small snippet of Python code that provides a prompt using the `Gradio` library:

```python

import gradio as gr

def generate_prompt():

return "Generative AI offers potential solutions for writing, reading, chatting, images, predictions, voice and image recognition, data analysis, graphs, statistics, maps, etc."

iface = gr.Interface(generate_prompt, "text")

iface.launch()

```

This code uses the `Gradio` library to create a simple web interface that displays the prompt when the code is run. The user can then interact with the prompt as needed.

Here's a small snippet of Python code to provide a RAG (Retrieval-Augmented Generation) using the `transformers` library:

```python

from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration

import torch

# Initialize the RAG tokenizer and retriever

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")

retriever = RagRetriever.from_pretrained("facebook/rag-token-base")

# Provide the input text

input_text = "Generative AI offers potential solutions for writing, reading, and chatting, but it goes far beyond that..."

# Encode the input text

input_ids = tokenizer(input_text, return_tensors="pt").input_ids

# Initialize the RAG model for generation

model = RagTokenForGeneration.from_pretrained("facebook/rag-token-base")

# Generate the RAG output

output = model.generate(input_ids)

# Decode and print the generated text

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)

```

This code uses the `transformers` library to initialize a RAG tokenizer, retriever, and model, then generates text based on the provided input. The RAG model leverages retriever to retrieve relevant passages from a knowledge source and then generate the text. This snippet provides a basic example of how to use RAG for text generation in Python.

To perform fine-tuning of a pre-trained model for generative AI applications in Python, you can use the following code as a starting point:

```python

from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments

# Load pre-trained model and tokenizer

model_name = "gpt2" # or any other pre-trained model

model = GPT2LMHeadModel.from_pretrained(model_name)

tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Define your custom dataset and data collator

dataset = TextDataset(tokenizer=tokenizer, file_path="your_custom_dataset.txt", block_size=128)

data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

# Define the training arguments

training_args = TrainingArguments(

output_dir="./fine_tuned_model",

overwrite_output_dir=True,

num_train_epochs=3,

per_device_train_batch_size=8,

save_steps=10_000,

save_total_limit=2,

)

# Create a Trainer and start the fine-tuning

trainer = Trainer(

model=model,

args=training_args,

data_collator=data_collator,

train_dataset=dataset,

)

trainer.train()

```

This code snippet uses the Hugging Face `transformers` library to fine-tune a pre-trained GPT-2 model on a custom dataset for language generation. It involves loading the pre-trained model and tokenizer, defining the custom dataset and data collator, setting the training arguments, and then initiating the fine-tuning process.

Fine-tuning pre-trained models is a reliable technique for creating high-performing generative AI applications. It involves updating pre-trained models with new information or data to customize them to a particular use case.

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[gtsai]

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[gtsai]

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[gtsai]

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[gtsai]

we expertly collect Video datasets, Image datasets, Text datasets, Healthcare datasets with the use of latest technology Our datasets are used in Face Detection, Face Recognition, OCR, Lidar and in many more Technologies.

[gtsai]

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Global Technology Solutions is an AI data collection Company that provides Data sets for machine learning. The data we collect is used for Artificial intelligence development and Machine Learning Models. we expertly collect Video datasets, Image datasets, Text datasets, Healthcaredatasets with the use of latest technology Our datasets are used in Face Detection, Face Recognition, OCR, Lidar and in many more Technologies.

[gtsai]

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[gtsai]

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[gtsai]

WHY MACHINE LEARNING IS IMPORTANT?

Machine learning is essential because the process or done

previously by humans, for example, customer service caller

bookkeeping and reviewing resume. by Machine learning it

can be transformed to a larger scale. why data sets are

essential for machine learning ? The data sets are

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the various actions performed by machine learning model.

First data set fed to an algorithm to check that the

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[gtsai]

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[gtsai]

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[gtsai]

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The text data is used in many AI/ML activities, for example-translation, sentiment analysis, spam filtering, etc. GTS is on the move to providing the best text collection services to make every computer vision project a huge success. Our data collection services are focused on creating the best database regardless of your AI model.

[gtsai]

GTS (Global Technology Solutions) is an AI data collection company that will provide you with accurate datasets for machine learning. They take the lead in AI data collection and have been very successful in a variety of data collections due to their expertise. GTS has enhanced systems of language, text, image, and video data collection, and such data is used for machine learning and AI development.

[gtsai]

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GTS (Global Technology Solutions) is an AI data collection company that will provide you with accurate datasets for machine learning. They take the lead in AI data collection and have been very successful in a variety of data collections due to their expertise. GTS has enhanced systems of language, text, image, and video data collection, and such data is used for machine learning and AI development. They provide data on numerous languages spoken globally and make use of them with undeniable expertise.