Model Card: GPT-2 Wiki Toxic

An in-domain GPT-2, pre-trained from scratch on the Wiki Toxic dataset text.

Model Details

Description

This model is based on the GPT-2 architecture and was pre-trained from scratch (in-domain) using the text in Wiki Toxic dataset, excluding its test split.

Checkpoints

Intermediate checkpoints from the pre-training process are available and can be accessed using specific tags, which correspond to training epochs and steps:

Epoch Step Tags
1 1496 epoch-1 step-1496
5 7480 epoch-5 step-7480
10 14960 epoch-10 step-14960
15 22440 epoch-15 step-22440
20 29920 epoch-20 step-29920
25 37400 epoch-25 step-37400
30 44880 epoch-30 step-44880
35 52360 epoch-35 step-52360
40 59840 epoch-40 step-59840
45 67320 epoch-45 step-67320
50 74800 epoch-50 step-74800

To load a model from a specific intermediate checkpoint, use the revision parameter with the corresponding tag:

from transformers import AutoModelForCausalLM

model = AutoModelForMaskedLM.from_pretrained("<model-name>", revision="<checkpoint-tag>")

Sources

  • Paper: [Information pending]

Training Details

For more details on the training procedure, please refer to the base model's documentation: Training procedure.

Training Data

All texts from Wiki Toxic dataset, excluding the test partition.

Preprocessing

All markup and symbols were removed from the texts, including punctuation.

Training Hyperparameters

  • Precision: fp16
  • Batch size: 8
  • Gradient accumulation steps: 12

Uses

For typical use cases and limitations, please refer to the base model's guidance: Inteded uses & limitations.

Bias, Risks, and Limitations

This model inherits potential risks and limitations from the base model. Refer to: Limitations and bias.

Environmental Impact

  • Hardware Type: NVIDIA A100 PCIE 40GB
  • Runtime: 21.5
  • Cluster Provider: Artemisa
  • Compute Region: EU
  • Carbon Emitted: 3.34 kg CO2 eq.

Citation

BibTeX:

[More Information Needed]

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