|
--- |
|
tags: |
|
- sentence-transformers |
|
- feature-extraction |
|
--- |
|
|
|
# TODO: Name of Model |
|
|
|
TODO: Description |
|
|
|
## Model Description |
|
TODO: Add relevant content |
|
|
|
(0) Base Transformer Type: DistilBertModel |
|
|
|
(1) Pooling mean |
|
|
|
(2) Dense 768x512 |
|
|
|
|
|
## Usage (Sentence-Transformers) |
|
|
|
Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed: |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can use the model like this: |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
sentences = ["This is an example sentence"] |
|
|
|
model = SentenceTransformer(TODO) |
|
embeddings = model.encode(sentences) |
|
print(embeddings) |
|
``` |
|
|
|
## TODO: Training Procedure |
|
|
|
## TODO: Evaluation Results |
|
|
|
## TODO: Citing & Authors |
|
|
