
# Model Description
BioDistilBERT-uncased is the result of training the [DistilBERT-uncased](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.) model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset. 


# Initialisation
We initialise our model with the pre-trained checkpoints of the [DistilBERT-uncased](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.) model available on Huggingface.

# Architecture
In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 30522. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters.

# Citation
If you use this model, please consider citing the following paper:

```bibtex
@misc{https://doi.org/10.48550/arxiv.2209.03182,
  doi = {10.48550/ARXIV.2209.03182},
  url = {https://arxiv.org/abs/2209.03182},
  author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A.},
  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, 68T50},
  title = {On the Effectiveness of Compact Biomedical Transformers},
  publisher = {arXiv},
  year = {2022}, 
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```
{"license": "mit", "title": "README", "emoji": "\ud83c\udfc3", "colorFrom": "gray", "colorTo": "purple", "sdk": "static", "pinned": false}
transformers pytorch distilbert fill-mask arxiv:2209.03182 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Model Description
BioDistilBERT-uncased is the result of training the DistilBERT-uncased model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset. 


# Initialisation
We initialise our model with the pre-trained checkpoints of the DistilBERT-uncased model available on Huggingface.

# Architecture
In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 30522. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters.

If you use this model, please consider citing the following paper:


# Model Description
BioDistilBERT-uncased is the result of training the DistilBERT-uncased model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset. # Initialisation
We initialise our model with the pre-trained checkpoints of the DistilBERT-uncased model available on Huggingface. # Architecture
In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 30522. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters.

If you use this model, please consider citing the following paper:
TAGS
#transformers #pytorch #distilbert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Model Description
BioDistilBERT-uncased is the result of training the DistilBERT-uncased model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset. # Initialisation
We initialise our model with the pre-trained checkpoints of the DistilBERT-uncased model available on Huggingface. # Architecture
In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 30522. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters.

If you use this model, please consider citing the following paper:
passage: TAGS
#transformers #pytorch #distilbert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Model Description
BioDistilBERT-uncased is the result of training the DistilBERT-uncased model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset.# Initialisation
We initialise our model with the pre-trained checkpoints of the DistilBERT-uncased model available on Huggingface.# Architecture
In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 30522. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters.

If you use this model, please consider citing the following paper: