TomData_-_GPT2-review-gguf

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GPT2-review - GGUF

• Model creator: https://huggingface.co/TomData/
• Original model: https://huggingface.co/TomData/GPT2-review/

Original model description:

datasets:

• McAuley-Lab/Amazon-Reviews-2023 language:
• en library_name: pytorch pipeline_tag: text-generation base_model: openai-community/gpt2-medium

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GPT-2 Medium - Review

Model Details

Model Description: This model is a checkpoint of GPT-2 Medium the 355M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a further pretrained model on a causal language modeling (CLM) objective with English Amazon Product Reviews from the Fashion category.

• Developed by: Students at University of Konstanz
• Model Type: Transformer-based language model
• Language(s): English
• Base Model: GPT2-medium
• Resources for more information: GitHub Repo

How to Get Started with the Model

Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:

>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='TomData/GPT2-review')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)

Here is how to use this model to get the features of a given text in PyTorch:

tokenizer = AutoTokenizer.from_pretrained("TomData/GPT2-review")
model = AutoModelForCausalLM.from_pretrained("TomData/GPT2-review")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

and in TensorFlow:

tokenizer = AutoTokenizer.from_pretrained("TomData/GPT2-review")
model = AutoModelForCausalLM.from_pretrained("TomData/GPT2-review")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)

Uses

This model is further pretrained to generate artificial product reviews. This can be usefull for:

• Market research
• Product analysis
• Customer preferences
• Fashion trends
• Research

Training

The model is further pretrained on the Amazion Review Dataset from McAuley-Lab. For training only the reviews related to the Amazon Fashion category are used. See:

dataset = load_dataset("McAuley-Lab/Amazon-Reviews-2023", "raw_review_Amazon_Fashion", trust_remote_code=True)