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gpt2-finetuned-wikitext2-test

Text Generation
Transformers
PyTorch
gpt2
Generated from Trainer
text-generation-inference
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metadata
license: mit
base_model: gpt2
datasets:
  - wikitext
language:
  - en
  - vi
metrics:
  - perplexity
library_name: transformers
pipeline_tag: text-generation
tags:
  - code
  - text-generation-inference
  - generated_from_trainer
model-index:
  - name: gpt2-finetuned-wikitext2
    results: []

gpt2-finetuned-wikitext2

This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: {loss: 3.6248738765716553, perplexity: 37.519989013671875}

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

  • Developed by: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: [More Information Needed]
  • Language(s) (NLP): [More Information Needed]
  • License: [More Information Needed]
  • Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

  • Training regime: [More Information Needed]

The following hyperparameters were used during training:

  • learning_rate: 5e-04
  • train_batch_size: 8
  • eval_batch_size: 8
  • optimizer: AdamW
  • lr_scheduler_type: linear
  • num_epochs: 2.0

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

Epoch Step Validation Loss
1.0 1000 3.6487
1.0 2000 3.6033
2.0 1000 3.6578
2.0 2000 3.6434

Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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