---
pipeline_tag: text-classification
tags:
- sentence-transformers
- text-classification
- sentimen-analysis
library_name: transformers
language:
- en
metrics:
- accuracy
- precision
- recall
---
# {MODEL_NAME}

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## 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 = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```



## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 14756 with parameters:
```
{"multi_target_strategy": "multi-output",
    "candidate_labels": ['atmosphere',  'beer list',  'bottomless brunch',  'breakfast',  'brunch',  'business',  'byob',  'casual',  'date night',  'delivery',  'fine dining',  'gluten free',  'good value',  'great cocktails',  'great service',  'happy hour',  'healthy',  'instagrammable',  'kid friendly',  'large groups',  'late night',  'live music',  'lunch',  'night out',  'outdoor seating',  'quick',  'romantic',  'rooftop',  'small plates',  'special event',  'sports/tvs',  'takeout',  'tasting menu',  'vegetarian',  'walk-in',  'wine list'],
   'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 

Parameters of the fit()-Method:
```
{
    "multi_target_strategy": "multi-output",
    "candidate_labels": ['atmosphere',  'beer list',  'bottomless brunch',  'breakfast',  'brunch',  'business',  'byob',  'casual',  'date night',  'delivery',  'fine dining',  'gluten free',  'good value',  'great cocktails',  'great service',  'happy hour',  'healthy',  'instagrammable',  'kid friendly',  'large groups',  'late night',  'live music',  'lunch',  'night out',  'outdoor seating',  'quick',  'romantic',  'rooftop',  'small plates',  'special event',  'sports/tvs',  'takeout',  'tasting menu',  'vegetarian',  'walk-in',  'wine list'],
    "epochs": 3,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": 14756,
    "warmup_steps": 1476,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BartModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->