README.md

---
pipeline_tag: text-classification
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- Setfit
language:
- en
library_name: sentence-transformers
---

# {Setfit_youtube_comments}

This is a [Setfit](https://github.com/huggingface/setfit) model: It maps sentences to a n dimensional dense vector space and
can be used for classification of text into question or not_question class.

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

## Usage (Sentence-Transformers)

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

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

Then you can use the model like this:

```python
from setfit import SetFitModel
model = SetFitModel.from_pretrained("tushifire/setfit_youtube_comments_is_a_question")

# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
print(preds)

preds = model(["""what video do I watch that takes the html_output and insert it into the actual html page?""",
               "Why does for loop end without a break statement"])
print(preds)
```


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 80 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 

Parameters of the fit()-Method:
```
{
    "epochs": 10,
    "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": 800,
    "warmup_steps": 80,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)
```

## Citing & Authors

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