library_name: transformers
language: en
license: apache-2.0
datasets:
- community-datasets/ohsumed
base_model:
- google-bert/bert-base-uncased
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.
- Developed by: Cesar Gonzalez-Gutierrez
- Funded by: ERC
- Architecture: BERT-base
- Language: English
- License: Apache 2.0
- Base model: BERT base model (uncased)
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]