Model Card: BERT-Ohsumed

An in-domain BERT-base model, pre-trained from scratch on the Ohsumed dataset text.

Model Details

Description

This model is based on the BERT base (uncased) architecture and was pre-trained from scratch (in-domain) using the text in Ohsumed dataset, excluding its test split. Only the masked language modeling (MLM) objective was used during pre-training.

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 98 epoch-1 step-98
5 490 epoch-5 step-490
10 980 epoch-10 step-980
20 1960 epoch-20 step-1960
30 2940 epoch-30 step-2940
40 3920 epoch-40 step-3920
50 4900 epoch-50 step-4900
60 5880 epoch-60 step-5880
70 6860 epoch-70 step-6860
80 7840 epoch-80 step-7840
90 8820 epoch-90 step-8820
100 9800 epoch-100 step-9800

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

from transformers import AutoModelForMaskedLM

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 Ohsumed dataset, excluding the test partition.

Training Hyperparameters

  • Precision: fp16
  • Batch size: 32
  • Gradient accumulation steps: 3

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 Tesla V100 PCIE 32GB
  • Cluster Provider: Artemisa
  • Compute Region: EU

Citation

BibTeX:

[More Information Needed]

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