
# DistilUSE Podcast Natural Questions

This is a [sentence-transformers](https://www.SBERT.net) model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as [described here](https://www.pinecone.io/learn/spotify-podcast-search/).

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["podcast about climate change", "how to make money on the internet"]

model = SentenceTransformer('pinecone/distiluse-podcast-nq')
embeddings = model.encode(sentences)
```

## Training
The model was trained with the parameters:

**DataLoader**:

`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 3748 with parameters:
```
{'batch_size': 64}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 1,
    "evaluation_steps": 0,
    "evaluator": "sentence_transformers.evaluation.RerankingEvaluator.RerankingEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 374,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```

## Citing & Authors

James Briggs, [How Spotify Uses Semantic Search for Podcasts](https://www.pinecone.io/learn/spotify-podcast-search/), Pinecone

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


# DistilUSE Podcast Natural Questions

This is a sentence-transformers model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as described here.

## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:



## Training
The model was trained with the parameters:

DataLoader:

'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 3748 with parameters:


Loss:

'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
  

Parameters of the fit()-Method:



## Full Model Architecture


## Citing & Authors

James Briggs, How Spotify Uses Semantic Search for Podcasts, Pinecone

# DistilUSE Podcast Natural Questions

This is a sentence-transformers model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as described here. ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## Training
The model was trained with the parameters:

DataLoader:

'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 3748 with parameters:


Loss:

'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
  

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors

James Briggs, How Spotify Uses Semantic Search for Podcasts, Pinecone
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us 
 # DistilUSE Podcast Natural Questions

This is a sentence-transformers model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as described here. ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## Training
The model was trained with the parameters:

DataLoader:

'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 3748 with parameters:


Loss:

'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
  

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors

James Briggs, How Spotify Uses Semantic Search for Podcasts, Pinecone
passage: TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us 
# DistilUSE Podcast Natural Questions

This is a sentence-transformers model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as described here.## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:## Training
The model was trained with the parameters:

DataLoader:

'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 3748 with parameters:


Loss:

'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
  

Parameters of the fit()-Method:## Full Model Architecture## Citing & Authors

James Briggs, How Spotify Uses Semantic Search for Podcasts, Pinecone