
# 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

{"tags": ["sentence-transformers", "feature-extraction"]}
sentence-transformers pytorch distilbert feature-extraction endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us 


# 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 installed:



Then you can use the model like this:



## TODO: Training Procedure

## TODO: Evaluation Results

## TODO: Citing & Authors

# 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 installed:



Then you can use the model like this: ## TODO: Training Procedure ## TODO: Evaluation Results ## TODO: Citing & Authors
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us 
 # 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 installed:



Then you can use the model like this: ## TODO: Training Procedure ## TODO: Evaluation Results ## TODO: Citing & Authors
passage: TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us 
# 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 installed:



Then you can use the model like this:## TODO: Training Procedure## TODO: Evaluation Results## TODO: Citing & Authors